In [1]:
!pip install lime

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: lime in /home/shehla.r/.local/lib/python3.8/site-packages (0.2.0.1)
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[notice] A new release of pip is available: 24.1.2 -> 25.0.1
[notice] To update, run: pip install --upgrade pip
In [3]:
!pip install openpyxl

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: openpyxl in /opt/apps/anaconda3/2020.11/lib/python3.8/site-packages (3.0.9)
Requirement already satisfied: et-xmlfile in /opt/apps/anaconda3/2020.11/lib/python3.8/site-packages (from openpyxl) (1.1.0)

[notice] A new release of pip is available: 24.1.2 -> 25.0.1
[notice] To update, run: pip install --upgrade pip
In [4]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN
from lime import lime_tabular

# Load the dataset
# file_path = '/content/Supplementary_Tables.xlsx'
file_path = 'Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1',engine= 'openpyxl')
In [5]:
# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Save feature names for later
feature_names = X.columns
In [6]:
# Function to trim feature names
def trim_feature_name(name):
    # Define the logic to trim the feature name
    # For example, keeping only the prefix up to the second underscore
    parts = name.split('_')
    if len(parts) > 2:
        return f"{parts[0]}_{parts[1]}"
    else:
        return name  # Return as is if the format is unexpected

# Apply the function to all feature names
trimmed_feature_names = [trim_feature_name(name) for name in feature_names]

# Update the feature names in the dataset
X.columns = trimmed_feature_names

# Scale the features
# scaler = StandardScaler()
# X_scaled = scaler.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X, Y, genes, test_size=0.2, random_state=42)
In [7]:
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
# Build the model
rf_clf = RandomForestClassifier(max_features=2, n_estimators =100 ,bootstrap = True)

rf_clf.fit(X_train, y_train)

# Make prediction on the testing data
y_pred = rf_clf.predict(X_test)

# Classification Report
print(classification_report(y_pred, y_test))

              precision    recall  f1-score   support

           0       0.97      0.85      0.91       406
           1       0.15      0.55      0.24        20

    accuracy                           0.84       426
   macro avg       0.56      0.70      0.58       426
weighted avg       0.94      0.84      0.88       426
In [8]:
!pip install shap

Defaulting to user installation because normal site-packages is not writeable
Collecting shap
  Downloading shap-0.44.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (24 kB)
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Downloading shap-0.44.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (538 kB)
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Downloading slicer-0.0.7-py3-none-any.whl (14 kB)
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Installing collected packages: slicer, numpy, shap
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
daal4py 2021.5.0 requires daal==2021.4.0, which is not installed.
tensorflow 2.13.1 requires numpy<=1.24.3,>=1.22, but you have numpy 1.20.3 which is incompatible.
tensorflow 2.13.1 requires typing-extensions<4.6.0,>=3.6.6, but you have typing-extensions 4.13.2 which is incompatible.
tensorflow-gpu 2.2.0 requires scipy==1.4.1; python_version >= "3", but you have scipy 1.10.1 which is incompatible.
tensorflow-gpu 2.2.0 requires tensorboard<2.3.0,>=2.2.0, but you have tensorboard 2.13.0 which is incompatible.
tensorflow-gpu 2.2.0 requires tensorflow-estimator<2.3.0,>=2.2.0, but you have tensorflow-estimator 2.13.0 which is incompatible.
jax 0.4.8 requires numpy>=1.21, but you have numpy 1.20.3 which is incompatible.
Successfully installed numpy-1.20.3 shap-0.44.1 slicer-0.0.7

[notice] A new release of pip is available: 24.1.2 -> 25.0.1
[notice] To update, run: pip install --upgrade pip
In [10]:
!pip install numpy>=1.21

[notice] A new release of pip is available: 24.1.2 -> 25.0.1
[notice] To update, run: pip install --upgrade pip
In [11]:
!pip install --upgrade pip

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: pip in /opt/apps/anaconda3/2020.11/lib/python3.8/site-packages (24.1.2)
Collecting pip
  Downloading pip-25.0.1-py3-none-any.whl.metadata (3.7 kB)
Downloading pip-25.0.1-py3-none-any.whl (1.8 MB)
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Installing collected packages: pip
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pipdeptree 2.23.1 requires packaging>=23.1, but you have packaging 21.3 which is incompatible.
Successfully installed pip-25.0.1

[notice] A new release of pip is available: 24.1.2 -> 25.0.1
[notice] To update, run: pip install --upgrade pip
In [13]:
# Define sampling techniques
def apply_sampling(X_train, y_train, technique):
    if technique == 'RandomOverSampler':
        sampler = RandomOverSampler(random_state=42)
    elif technique == 'SMOTE':
        sampler = SMOTE(random_state=42)
    elif technique == 'ADASYN':
        sampler = ADASYN(random_state=42)
    else:
        raise ValueError("Unknown sampling technique")

    X_train_resampled, y_train_resampled = sampler.fit_resample(X_train, y_train)
    return X_train_resampled, y_train_resampled
In [14]:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel, RFE
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.metrics import (accuracy_score, confusion_matrix, f1_score,
                             matthews_corrcoef, roc_auc_score, recall_score)
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN
import lime
from lime import lime_tabular

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Define best-first search feature selection
def best_first_search_feature_selection(X_train, y_train, feature_names):
    model = RandomForestClassifier(random_state=42)
    selector = RFE(model, n_features_to_select=10)  # Specify the number of features to select
    selector = selector.fit(X_train, y_train)

    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]
In [15]:
# Evaluate the model with Grid Search and cross-validation
def evaluate_model_grid_search(X, y, model_name, feature_names):
    param_grid = {
        'n_estimators': [100, 200, 300],
        'max_depth': [None, 10, 20, 30],
        'min_samples_split': [2, 5, 10]
    }

    # Use GridSearchCV for hyperparameter tuning
    grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                               param_grid=param_grid,
                               cv=10,  # 10-fold CV
                               scoring='accuracy')

    grid_search.fit(X, y)

    print(f"Model: {model_name}")
    print(f"Best parameters: {grid_search.best_params_}")
    print(f"Best cross-validated accuracy: {grid_search.best_score_:.4f}")

    return grid_search.best_estimator_

Lime explanation¶

In [17]:
# Sampling techniques to apply
# sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']
sampling_techniques = ['RandomOverSampler']
for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train.values, y_train.values, technique)

    # Apply best-first search for feature selection
    X_train_bfs, X_test_bfs, bfs_feature_names = best_first_search_feature_selection(X_train_resampled, y_train_resampled, trimmed_feature_names)

    # Define models with trimmed feature names
    models = {
        "Best-First Search Feature Selection": (X_train_bfs, X_test_bfs, bfs_feature_names)
    }

    for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
        model = evaluate_model_grid_search(X_train_fs, y_train_resampled, name, fs_feature_names)

        # Create a LIME explainer with trimmed feature names
        explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

        # Explain a prediction
        for i in range(2):  # Explain the first 10 instances
            exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)
            exp.show_in_notebook(show_all=False)
            print(f"Gene being explained: {genes_test.iloc[i]}")

            # Show LIME prediction and compare with actual label
            lime_prediction = model.predict([X_test_fs[i]])[0]
            actual_label = y_test.iloc[i]
            print(f"LIME Prediction: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual Label: {'ASD' if actual_label == 1 else 'Non-ASD'}")
            print(f"Prediction Match: {lime_prediction == actual_label}")
            print("-" * 2)

Applying RandomOverSampler

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RFE was fitted without feature names
  warnings.warn(

Model: Best-First Search Feature Selection
Best parameters: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}
Best cross-validated accuracy: 0.9502

Gene being explained: REEP3
LIME Prediction: Non-ASD, Actual Label: ASD
Prediction Match: False
--

Gene being explained: TNFSF11
LIME Prediction: Non-ASD, Actual Label: Non-ASD
Prediction Match: True
--
In [18]:
# Import necessary libraries
from collections import defaultdict
import numpy as np

# Dictionary to store feature importances
feature_importance_summary = defaultdict(list)

# Sampling techniques to apply
# sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']
sampling_techniques = ['RandomOverSampler']
for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train.values, y_train.values, technique)

    # Apply best-first search for feature selection
    X_train_bfs, X_test_bfs, bfs_feature_names = best_first_search_feature_selection(X_train_resampled, y_train_resampled, trimmed_feature_names)

    # Define models with trimmed feature names
    models = {
        "Best-First Search Feature Selection": (X_train_bfs, X_test_bfs, bfs_feature_names)
    }

    for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
        model = evaluate_model_grid_search(X_train_fs, y_train_resampled, name, fs_feature_names)

        # Create a LIME explainer with trimmed feature names
        explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

        # Explain predictions for the first 50 instances in the test set
        for i in range(5):  # Adjust the range for larger sample sizes if needed
            exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)

            # Get LIME feature importances and add them to the summary
            for feature, importance in exp.as_list():
                feature_importance_summary[feature].append(importance)

# After looping through all instances, calculate average importance for each feature
average_feature_importance = {feature: np.mean(importances) for feature, importances in feature_importance_summary.items()}
sorted_importances = sorted(average_feature_importance.items(), key=lambda x: abs(x[1]), reverse=True)

# Display top contributing features
print("Top contributing features to ASD prediction:")
for feature, avg_importance in sorted_importances[:10]:  # Adjust the number for more features
    print(f"Feature: {feature}, Average Importance: {avg_importance}")

Applying RandomOverSampler

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RFE was fitted without feature names
  warnings.warn(

Model: Best-First Search Feature Selection
Best parameters: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}
Best cross-validated accuracy: 0.9502
Top contributing features to ASD prediction:
Feature: M1C_8yrs <= 0.60, Average Importance: 0.05492335014309474
Feature: HIP_2yrs <= 0.36, Average Importance: 0.05428448925478292
Feature: HIP_2yrs > 2.63, Average Importance: -0.05150724197235641
Feature: M1C_8yrs > 3.16, Average Importance: -0.05086567920611206
Feature: MFC_21pcw <= 0.51, Average Importance: -0.05071652250574388
Feature: OFC_8pcw <= 0.53, Average Importance: 0.04190339508018101
Feature: VFC_21yrs <= 0.57, Average Importance: -0.04097536018599875
Feature: VFC_21yrs > 3.63, Average Importance: 0.04035026380384895
Feature: 0.57 < VFC_21yrs <= 2.16, Average Importance: -0.04022912870788327
Feature: 2.16 < VFC_21yrs <= 3.63, Average Importance: 0.038173881507958675
In [24]:
# !pip install xgboost
# from xgboost import XGBClassifier
# from sklearn.metrics import (accuracy_score, confusion_matrix, f1_score, matthews_corrcoef,
#                              roc_auc_score, recall_score, classification_report)

# # Function to calculate performance metrics
# def calculate_metrics(y_true, y_pred, y_prob, model_name):
#     conf_matrix = confusion_matrix(y_true, y_pred)
#     accuracy = accuracy_score(y_true, y_pred)
#     mcc = matthews_corrcoef(y_true, y_pred)
#     roc_auc = roc_auc_score(y_true, y_prob)
#     sensitivity = recall_score(y_true, y_pred)
#     specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
#     g_mean = (sensitivity * specificity) ** 0.5

#     print(f"Model: {model_name}")
#     print(f"Accuracy: {accuracy:.4f}")
#     print(f"MCC: {mcc:.4f}")
#     print(f"AUC ROC: {roc_auc:.4f}")
#     print(f"Sensitivity: {sensitivity:.4f}")
#     print(f"Specificity: {specificity:.4f}")
#     print(f"G Mean: {g_mean:.4f}")
#     print("-" * 40)

#     return accuracy, specificity, sensitivity, mcc, roc_auc

# # Function to evaluate model and calculate metrics
# def evaluate_model(X_train, X_test, y_train, y_test, model_name):
#     model = RandomForestClassifier(random_state=42)
#     # model = XGBClassifier(random_state=42)
#     model.fit(X_train, y_train)

#     y_pred = model.predict(X_test)
#     y_prob = model.predict_proba(X_test)[:, 1]  # Probability for class 1 (ASD)

#     return calculate_metrics(y_test, y_pred, y_prob, model_name)

# # Sampling techniques to apply
# sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']

# for technique in sampling_techniques:
#     print(f"Applying {technique}")

#     # Apply sampling technique
#     X_train_resampled, y_train_resampled = apply_sampling(X_train.values, y_train.values, technique)

#     # Full feature set evaluation
#     print("Evaluating on Full Feature Set")
#     evaluate_model(X_train_resampled, X_test, y_train_resampled, y_test, "Full Feature Set")

# #     # Lasso feature selection
#     X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names, prefit=True)
#     print("Evaluating after Lasso Feature Selection")
#     evaluate_model(X_train_lasso, X_test_lasso, y_train_resampled, y_test, "Lasso Feature Selection")

#     # Random Forest feature selection
#     X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)
#     print("Evaluating after Random Forest Feature Selection")
#     evaluate_model(X_train_rf, X_test_rf, y_train_resampled, y_test, "Random Forest Feature Selection")

#     # Best-First Search feature selection
#     X_train_bfs, X_test_bfs, bfs_feature_names = best_first_search_feature_selection(X_train_resampled, y_train_resampled, trimmed_feature_names)
#     print("Evaluating after Best-First Search Feature Selection")
#     evaluate_model(X_train_bfs, X_test_bfs, y_train_resampled, y_test, "Best-First Search Feature Selection")

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: xgboost in /home/shehla.r/.local/lib/python3.8/site-packages (2.1.4)
Requirement already satisfied: numpy in /home/shehla.r/.local/lib/python3.8/site-packages (from xgboost) (1.20.3)
Requirement already satisfied: nvidia-nccl-cu12 in /home/shehla.r/.local/lib/python3.8/site-packages (from xgboost) (2.27.5)
Requirement already satisfied: scipy in /home/shehla.r/.local/lib/python3.8/site-packages (from xgboost) (1.10.1)
Applying RandomOverSampler
Evaluating on Full Feature Set

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(

Model: Full Feature Set
Accuracy: 0.8521
MCC: 0.3984
AUC ROC: 0.8462
Sensitivity: 0.3944
Specificity: 0.3944
G Mean: 0.3944
----------------------------------------

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_175865/1472039280.py in <module>
     55 
     56     # Random Forest feature selection
---> 57     X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)
     58     print("Evaluating after Random Forest Feature Selection")
     59     evaluate_model(X_train_rf, X_test_rf, y_train_resampled, y_test, "Random Forest Feature Selection")

/tmp/ipykernel_175865/2610247422.py in rf_feature_selection(X_train, y_train, X_test, feature_names)
     24     rf_model.fit(X_train, y_train)
     25     selector = SelectFromModel(rf_model, threshold='mean')
---> 26     selected_indices = selector.get_support(indices=True)
     27     return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]
     28 

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/feature_selection/_base.py in get_support(self, indices)
     51             values are indices into the input feature vector.
     52         """
---> 53         mask = self._get_support_mask()
     54         return mask if not indices else np.where(mask)[0]
     55 

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/feature_selection/_from_model.py in _get_support_mask(self)
    204             estimator = self.estimator_
    205         else:
--> 206             raise ValueError(
    207                 "Either fit the model before transform or set"
    208                 ' "prefit=True" while passing the fitted'

ValueError: Either fit the model before transform or set "prefit=True" while passing the fitted estimator to the constructor.

Feature Selection Techniques¶

In [28]:
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (accuracy_score, confusion_matrix, f1_score, matthews_corrcoef,
                             roc_auc_score, recall_score, classification_report)
from sklearn.feature_selection import SelectFromModel
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN

# Function to apply sampling
def apply_sampling(X_train, y_train, technique):
    if y_train.name is None:
        y_train.name = 'target'
    
    if technique == 'RandomOverSampler':
        sampler = RandomOverSampler(random_state=42)
    elif technique == 'SMOTE':
        sampler = SMOTE(random_state=42)
    elif technique == 'ADASYN':
        sampler = ADASYN(random_state=42)
    else:
        raise ValueError("Unknown sampling technique")
    
    return sampler.fit_resample(X_train.values, y_train.values)

# Lasso feature selection (assumed correct elsewhere)
def lasso_feature_selection(X_train, y_train, X_test, feature_names, prefit=False):
    from sklearn.linear_model import LassoCV
    lasso_model = LassoCV(cv=5, random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean', prefit=True)
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Random Forest feature selection (FIXED)
def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean', prefit=True)
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Placeholder for Best-First Search feature selection
def best_first_search_feature_selection(X_train, y_train, feature_names):
    # Dummy logic for demonstration
    from sklearn.feature_selection import SelectKBest, f_classif
    selector = SelectKBest(score_func=f_classif, k=10)
    selector.fit(X_train, y_train)
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Function to calculate performance metrics
def calculate_metrics(y_true, y_pred, y_prob, model_name):
    conf_matrix = confusion_matrix(y_true, y_pred)
    accuracy = accuracy_score(y_true, y_pred)
    mcc = matthews_corrcoef(y_true, y_pred)
    roc_auc = roc_auc_score(y_true, y_prob)
    sensitivity = recall_score(y_true, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    return accuracy, specificity, sensitivity, mcc, roc_auc

# Function to evaluate model and calculate metrics
def evaluate_model(X_train, X_test, y_train, y_test, model_name):
    model = RandomForestClassifier(random_state=42)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]  # Probability for class 1
    return calculate_metrics(y_test, y_pred, y_prob, model_name)

# Main loop
sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']

# Set y_train.name if missing
y_train.name = 'target'

for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling
    X_train_resampled, y_train_resampled = apply_sampling(X_train, y_train, technique)

    # Full feature set evaluation
    print("Evaluating on Full Feature Set")
    evaluate_model(X_train_resampled, X_test, y_train_resampled, y_test, "Full Feature Set")

    # Lasso
    X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(
        X_train_resampled, y_train_resampled, X_test, trimmed_feature_names
    )
    print("Evaluating after Lasso Feature Selection")
    evaluate_model(X_train_lasso, X_test_lasso, y_train_resampled, y_test, "Lasso Feature Selection")

    # RF
    X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(
        X_train_resampled, y_train_resampled, X_test, trimmed_feature_names
    )
    print("Evaluating after Random Forest Feature Selection")
    evaluate_model(X_train_rf, X_test_rf, y_train_resampled, y_test, "Random Forest Feature Selection")

    # BFS
    X_train_bfs, X_test_bfs, bfs_feature_names = best_first_search_feature_selection(
        X_train_resampled, y_train_resampled, trimmed_feature_names
    )
    print("Evaluating after Best-First Search Feature Selection")
    evaluate_model(X_train_bfs, X_test_bfs, y_train_resampled, y_test, "Best-First Search Feature Selection")

Applying RandomOverSampler
Evaluating on Full Feature Set

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(

Model: Full Feature Set
Accuracy: 0.8521
MCC: 0.3984
AUC ROC: 0.8462
Sensitivity: 0.3944
Specificity: 0.3944
G Mean: 0.3944
----------------------------------------

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.09737053977960386, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11393782219658988, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.09895846323854585, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18133207573930576, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20261139434768438, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2733823399830726, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3304644104284762, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.25558769643458845, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13430684119674652, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20766386745145837, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.25506851726891, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3335760809760586, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.515005709794309, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6421381631420218, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8272957904466125, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7566826300104594, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.096194265865165, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3505292646026987, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9616428814226197, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9011416826562595, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8392172133749227, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.190714386693088, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3499409589871902, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3132208861294146, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2732061545005422, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.611589796835176, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0790015027743607, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.568510811658939, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.705317627542911, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.327501798477016, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.045199921127676, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.433285524049495, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.484240625327544, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.53249932719163, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.062126252165854, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.363968446803852, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.22688611668525, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.836111652549164, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.60712599121888, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.05700493507771398, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.057283521101624046, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07987086243355179, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.10466339984861861, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18571173687172404, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.28177561976639254, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.30208694564873895, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.32156540617882, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23562307688530382, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14790368235662754, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2881071481971844, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3141880948087419, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6592076775923488, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7173197474434687, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8126503172248931, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6057224280759783, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7338562513998568, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1781566848487728, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1214322461529491, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1160271032161404, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.385379359415026, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.39406672605719, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2279290749845586, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6264429107931164, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2587554658233273, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6870955291953464, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.67529328159884, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.383833180279396, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.391512628817935, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.7282763066479845, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.971803868924269, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.985429106668761, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.698537555791347, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.528482478549591, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.411694054408173, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.267218652325596, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.383171743698654, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.875594030044965, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.75484793473521, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.102392079456962, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07175573798792811, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1339099913485029, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21913878733590764, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1787620012185016, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3216455462104477, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22333982093971372, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23946126958412606, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2000028949050261, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.32927051054070944, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.34849137418325427, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.35430301886299276, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.46450540233814763, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.42756417999180485, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5258113443599086, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6193012579271908, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6368671230350174, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8776220877331298, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8636590365047709, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8719943390316871, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4838731824185345, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4751515550854322, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3097492805825368, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5388615795234273, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7328149739943512, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.303958626337959, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1099004822437735, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.877500541944386, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.148215363903432, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.602543641767824, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.2505640734051156, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.736987527310674, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.189320801767849, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.105879652209154, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.511044534844501, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.780623669117716, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.932031633364517, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.046359845658827, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.47679505274354, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07085736036310664, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.10357434659158571, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1580759164664869, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16447330327292775, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2362160343315054, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2052935809929295, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2422418017015957, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.32132320737389364, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.27548326206840557, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3147934168720212, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4797488271858583, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3724627978273247, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.38694743648119356, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.45543369082412255, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4278490213777104, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.48695074862695265, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5809309858020129, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6439494434381459, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7276012369347313, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6559583367605626, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9719796857231984, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8634162576797166, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2239929616912946, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0252345570874866, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.50564010396954, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.103483459526302, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.561488135227961, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.671089445797975, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.796153080156927, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.056334937754372, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.13622665804013, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.761149482713478, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.387606556443274, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.840391544076397, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.760347186444307, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.13911920412906, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.06586297724908263, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12126910069349606, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1234683039964466, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16363872059190498, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24546878414344064, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2753000266550316, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.26836362062789476, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21305226403489996, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2811806657430793, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16356654560206607, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3511513136867279, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21681672243619232, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3812295126660956, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.44986117419580296, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7001663005447085, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.832104799841261, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7814931562206198, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9262196872076629, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1464609288723864, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.06471613027486, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2185186849202694, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2515387419921922, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.420381509763729, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8113510224123388, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.384473072591902, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.400746937651121, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.394265678741789, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9272662947930144, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.736894500111134, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3279618329931395, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.490562376004817, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.325218306934488, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.783552063000343, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.396541642844682, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.214722370946873, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+02, tolerance: 7.035e-02
  model = cd_fast.enet_coordinate_descent(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Evaluating after Lasso Feature Selection
Model: Lasso Feature Selection
Accuracy: 0.8427
MCC: 0.3518
AUC ROC: 0.8293
Sensitivity: 0.3521
Specificity: 0.3521
G Mean: 0.3521
----------------------------------------

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Evaluating after Random Forest Feature Selection
Model: Random Forest Feature Selection
Accuracy: 0.8568
MCC: 0.4527
AUC ROC: 0.8435
Sensitivity: 0.4930
Specificity: 0.4930
G Mean: 0.4930
----------------------------------------
Evaluating after Best-First Search Feature Selection

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectKBest was fitted without feature names
  warnings.warn(

Model: Best-First Search Feature Selection
Accuracy: 0.8122
MCC: 0.2683
AUC ROC: 0.7762
Sensitivity: 0.3380
Specificity: 0.3380
G Mean: 0.3380
----------------------------------------
Applying SMOTE
Evaluating on Full Feature Set

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(

Model: Full Feature Set
Accuracy: 0.8028
MCC: 0.3663
AUC ROC: 0.8299
Sensitivity: 0.5493
Specificity: 0.5493
G Mean: 0.5493
----------------------------------------

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12457072408051317, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17645534962946385, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15704179223860137, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15491288509480228, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11804454929222175, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.40739400025370287, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3391513998751634, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5092101539642044, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.47509474096040094, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5503386506777588, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6110646322515549, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5442945171125757, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4508220907572422, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3856304997001416, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5943982818558311, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4338522793228776, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6667265414903341, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9580915930825711, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9412183602069035, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8587753996814342, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.678011664555271, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0954523161327643, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.69527055238035, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.762669457825467, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.475444411484233, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.308961267727, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.214183296567882, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.646019698959606, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.510393585224534, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0431230964321685, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.564357715217739, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.1368932531241285, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.879122274102627, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.6140364039473525, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.401337627729561, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.46330211930632, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.44362870340845, tolerance: 0.05487534429142603
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13757853265673248, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.32505256802505755, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.46845947188649006, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4424372989448102, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3455961930912963, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.31631293972111507, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.28063642664596955, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.39639364155391377, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.36063245937538113, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.32165236658931917, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5327355937450875, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4877640277615001, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5437558710359554, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6517728808631773, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6488874579471258, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9505273470466591, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7282334248868665, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9332723668140659, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4765580103413072, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1216630536888488, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.815287027013767, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.509613060109672, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.984790103260252, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.412034613122387, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.718563205120745, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.799956298508562, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.692677062481323, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.995098575145676, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.930946406425477, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.029939866532004, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.965933376425198, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.783728459649353, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.409384219820993, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.279717018578253, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.177663044249385, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.13191314871969, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.077069207799184, tolerance: 0.0550136828076411
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.05633100178681616, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14999883767109168, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2865107231778552, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5399921791583893, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.333994007364538, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6260608912720613, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.46761092440931407, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5272602561833537, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.551717262712657, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5032204427965894, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5066731562098425, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8399590960161731, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8304767206773249, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.016972962072657, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4066759535703, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2948285184873498, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2430186657458933, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9594011948904324, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7736957844049073, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6329842222751836, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.048444675159857, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.468809343717851, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6147407153481765, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.460653092473933, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.675200240123161, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.190246320308972, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.115379011713287, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.554046274115478, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.404007612320299, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.740757209367416, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.5597471249649, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21.88748777735489, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.27776697005112, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.712274705094018, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.894772324615275, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.384339989605678, tolerance: 0.054398933807196806
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.10386154796157143, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15335181912774942, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15479783826822313, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4859407323336882, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.41332962521107675, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23773213223836365, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3051216784004964, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.38141019147803945, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.381001280931514, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.29225563660196485, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.38166700803361664, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6016642845965521, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5630243075472663, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7205245694194673, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5506905238696618, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6486015606575961, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5288114319643, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7607707740634737, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.962252899228929, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6194559949789777, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6233357797934218, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.220970859458646, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0053274116106365, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.265203570499125, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1253032824377556, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.199873273083057, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.164013922930735, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.745875439769208, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.680902572849988, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.130760264967364, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.620622764326981, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.616762593843134, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.37057655299418, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.959214334892408, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.879627213708744, tolerance: 0.053024433585073215
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11285151516102587, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1461631263841241, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17924215000618915, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2216623529983508, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.28774370276329364, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22918620845575788, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.29923875261755484, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5073507224993818, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5085802283211933, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5251137128846608, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.43612907960221037, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.35107829349402664, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6189629788416369, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6032633677534136, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4561499797804913, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5005751834233934, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9779409721623438, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7615351429080306, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8307895646986481, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1325667518480032, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2957668435366259, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.237211411407344, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5487450489264631, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4849909757283513, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.278531986210851, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.341471536611351, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.916341433550059, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.65041796457993, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.874955336486721, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.509181188821145, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.012955575406778, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.870953791284137, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.379791295104752, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.999272686153461, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.944349278666863, tolerance: 0.05279373889875674
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.478e+01, tolerance: 7.035e-02
  model = cd_fast.enet_coordinate_descent(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Evaluating after Lasso Feature Selection
Model: Lasso Feature Selection
Accuracy: 0.8239
MCC: 0.4253
AUC ROC: 0.8324
Sensitivity: 0.5915
Specificity: 0.5915
G Mean: 0.5915
----------------------------------------

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Evaluating after Random Forest Feature Selection
Model: Random Forest Feature Selection
Accuracy: 0.8028
MCC: 0.3663
AUC ROC: 0.8336
Sensitivity: 0.5493
Specificity: 0.5493
G Mean: 0.5493
----------------------------------------
Evaluating after Best-First Search Feature Selection

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectKBest was fitted without feature names
  warnings.warn(

Model: Best-First Search Feature Selection
Accuracy: 0.6948
MCC: 0.2664
AUC ROC: 0.7644
Sensitivity: 0.6338
Specificity: 0.6338
G Mean: 0.6338
----------------------------------------
Applying ADASYN
Evaluating on Full Feature Set

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1757794776098649, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12839812494036096, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15885129726234481, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23449146951986677, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(

Model: Full Feature Set
Accuracy: 0.8146
MCC: 0.3799
AUC ROC: 0.8260
Sensitivity: 0.5352
Specificity: 0.5352
G Mean: 0.5352
----------------------------------------

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2617985750892444, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2466262064128273, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2950212302959585, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2022775318161507, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2778234759792326, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.37648800084036793, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2942580708394189, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2721404421936313, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.31046513225356875, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.40018732009815494, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5287216726469524, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8103950542694633, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9593079206676975, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8188601901596257, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7811228273201323, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7924511765615989, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2468923061467194, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5355129830944065, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7501919273278759, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1749343405682566, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6421890798969514, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0708093694030367, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.74123804505075, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3428210705750985, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.559606518529563, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.185070783000754, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.828008080330704, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.430993379191477, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.245107448979184, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.984154677247375, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.355838778350403, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.334211056355883, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.836059926549353, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.657708777987864, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.274826201513122, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.370042372576393, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.29058106768673, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.0163585064264, tolerance: 0.05430149118843201
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.06868828796461912, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07441544606359685, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17491834032381348, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24682048388729072, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22769709300087015, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3858258290654817, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.36427803187359586, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.38639679509549296, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4142718923133657, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.39040839256801974, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.27056492651206554, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5716166805522107, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6999246554179308, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4618552825294273, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5406167742672778, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.49217720038217294, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4404148803470207, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8809255657533299, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4243326268193073, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4032793997619706, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3258260713512868, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4186650115559019, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9928255193449615, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6016350805963384, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.720716318530407, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4858667675513004, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.161008199476015, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.554377130803459, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.143604780193499, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.015965226660285, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.742246995589028, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.316820022175705, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.524957114847325, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.829538141741864, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.106507229455872, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.713539470363855, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.160249105468353, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.08776475600979, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.077249895832324, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.139015258805216, tolerance: 0.05442024401265248
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.07399734333949937, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.08650250847654206, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15891001494463808, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23850420198857591, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2794421775718092, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24748044449870576, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.30447215863352994, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3256916125332623, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5696363740805168, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5573350317078507, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6271584522660305, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.924411236345577, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6342962968336678, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7283844063545075, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.864195405951989, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8697814238685737, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5053381057558113, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3763056937477245, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4945873163968315, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.033971009283448, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6207246478823834, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8571373614479967, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.847226851318794, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.570681680365567, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.036948831429044, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.58644252428536, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8602589226911164, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2052620139639885, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.795788123927309, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.086428446744492, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.741053167542361, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.250737744683363, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.786642146333264, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.910783531737252, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.682284516893617, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.364163951092735, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.223938547645446, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.985121457769452, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.402034983821324, tolerance: 0.05390280036133702
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.06206881134892228, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1038870542896575, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22437970616135772, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2611070637858006, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23333477881965337, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22226575772037904, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18750867752720524, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2336521375659686, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22454837168379527, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22720123205419895, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.43646904591963676, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4800172902100712, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.43794692362837395, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.44949413961325035, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.489401541148311, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5097355549304439, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0501545505937884, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1047840431315308, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1869462720103456, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3596695299856094, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.414050289234069, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9367272009731948, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4915076179620144, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.420346417416795, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9338329424130336, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.826913434995419, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.659158894948035, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.313987149466527, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.291900237895959, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.243700782579708, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.60727029844432, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.943163708653714, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.525158175926123, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.840685292214204, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.1403271149378895, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.3919097269612735, tolerance: 0.05217813911472456
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14151362367550746, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2753430770275145, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2560185469071712, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24436764207666783, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3170345792177045, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.32460436040389595, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21887075022067393, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22594572029072424, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24928481144849002, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3421087510076859, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.38037142496148135, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3144467746766395, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.46593876089195874, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5727100286053428, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4536021749719339, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.734210692338138, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6583174867226944, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6480853505496498, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6618883047270856, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9296559799989836, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.355281924315051, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7948784940277278, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.757600744265801, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9607586853155965, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0839528705365353, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.353128355392016, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.461084040093681, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.14568470954859, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.550491598221157, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8674104640328437, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.792768949067579, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2497484771454594, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.217903889008056, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.5873354416308985, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.227835174811105, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.997860861284096, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.632895345835067, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.356926828681878, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.400979080879665, tolerance: 0.05128495934959351
  model = cd_fast.enet_coordinate_descent_gram(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.272e+01, tolerance: 6.916e-02
  model = cd_fast.enet_coordinate_descent(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Evaluating after Lasso Feature Selection
Model: Lasso Feature Selection
Accuracy: 0.8122
MCC: 0.3828
AUC ROC: 0.8289
Sensitivity: 0.5493
Specificity: 0.5493
G Mean: 0.5493
----------------------------------------

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Evaluating after Random Forest Feature Selection
Model: Random Forest Feature Selection
Accuracy: 0.8099
MCC: 0.4000
AUC ROC: 0.8338
Sensitivity: 0.5915
Specificity: 0.5915
G Mean: 0.5915
----------------------------------------
Evaluating after Best-First Search Feature Selection

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SelectKBest was fitted without feature names
  warnings.warn(

Model: Best-First Search Feature Selection
Accuracy: 0.7254
MCC: 0.2542
AUC ROC: 0.7491
Sensitivity: 0.5493
Specificity: 0.5493
G Mean: 0.5493
----------------------------------------
In [29]:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc

# Function to plot AUC-ROC curve
def plot_auc_roc(y_true, y_prob, label, color, linestyle='-'):
    fpr, tpr, _ = roc_curve(y_true, y_prob)
    roc_auc = auc(fpr, tpr)
    plt.plot(fpr, tpr, label=f'{label} (AUC = {roc_auc:.4f})', color=color, linestyle=linestyle)

# Function to evaluate model and plot AUC-ROC curve
def evaluate_and_plot_roc(X_train, X_test, y_train, y_test, model_name, label, color, linestyle='-'):
    model = RandomForestClassifier(random_state=42)
    # model = XGBClassifier(random_state=42)
    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]  # Probability for class 1 (ASD)

    # Calculate metrics
    calculate_metrics(y_test, y_pred, y_prob, model_name)

    # Plot AUC-ROC
    plot_auc_roc(y_test, y_prob, label, color, linestyle)

# Plotting AUC-ROC curves for different feature sets
plt.figure(figsize=(8, 7))

# Full feature set
print("Evaluating on Full Feature Set")
evaluate_and_plot_roc(X_train_resampled, X_test, y_train_resampled, y_test,
                      "Full Feature Set", label="Full Feature Set", color='blue')

# Lasso feature selection
# print("Evaluating after Lasso Feature Selection")
# evaluate_and_plot_roc(X_train_lasso, X_test_lasso, y_train_resampled, y_test,
#                       "Lasso Feature Selection", label="Lasso Feature Set", color='green')

# Random Forest feature selection
print("Evaluating after Random Forest Feature Selection")
evaluate_and_plot_roc(X_train_rf, X_test_rf, y_train_resampled, y_test,
                      "Random Forest Feature Selection", label="Random Forest Feature Set", color='orange')

# Best-First Search feature selection
# print("Evaluating after Best-First Search Feature Selection")
# evaluate_and_plot_roc(X_train_bfs, X_test_bfs, y_train_resampled, y_test,
#                       "Best-First Search Feature Selection", label="Best-First Search Feature Set", color='red')

# Finalize the plot
plt.plot([0, 1], [0, 1], 'k--', label='Random Chance')
# plt.title('AUC-ROC Curve Comparison', fontsize=16)
plt.xlabel('False Positive Rate (FPR)', fontsize=14)
plt.ylabel('True Positive Rate (TPR)', fontsize=14)
plt.legend(fontsize=12)
plt.grid(alpha=0.5)
plt.show()

Evaluating on Full Feature Set

/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(
/opt/apps/anaconda3/2020.11/lib/python3.8/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names
  warnings.warn(

Model: Full Feature Set
Accuracy: 0.8146
MCC: 0.3799
AUC ROC: 0.8260
Sensitivity: 0.5352
Specificity: 0.5352
G Mean: 0.5352
----------------------------------------
Evaluating after Random Forest Feature Selection
Model: Random Forest Feature Selection
Accuracy: 0.8099
MCC: 0.4000
AUC ROC: 0.8338
Sensitivity: 0.5915
Specificity: 0.5915
G Mean: 0.5915
----------------------------------------

10 fold Cross-validation¶

In [31]:
from sklearn.metrics import accuracy_score, confusion_matrix, matthews_corrcoef, roc_auc_score, recall_score

# Function to calculate performance metrics with standard deviation and mean
def calculate_metrics_with_std(y_true, y_pred, y_prob, model_name):
    # Calculate confusion matrix and other metrics
    conf_matrix = confusion_matrix(y_true, y_pred)
    accuracy = accuracy_score(y_true, y_pred)
    mcc = matthews_corrcoef(y_true, y_pred)
    roc_auc = roc_auc_score(y_true, y_prob)
    sensitivity = recall_score(y_true, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    # Print the metrics
    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    return accuracy, specificity, sensitivity, mcc, roc_auc, g_mean

# Include standard deviation and mean for GridSearchCV cross-validation
def evaluate_grid_search_with_cv(X_train, y_train, X_test, y_test, model_name):
    # Define parameter grid
    param_grid = {
        'n_estimators': [100, 200, 300],
        'max_depth': [None, 10, 20, 30],
        'min_samples_split': [2, 5, 10]
    }

    # Initialize Random Forest
    rf_model = RandomForestClassifier(random_state=42)

    # GridSearchCV for hyperparameter tuning with 10-fold cross-validation
    grid_search = GridSearchCV(estimator=rf_model,
                               param_grid=param_grid,
                               cv=10,  # 10-fold cross-validation
                               scoring='accuracy',
                               return_train_score=True)  # To access train score for each fold

    # Fit the model
    grid_search.fit(X_train, y_train)

    # Extract results from GridSearchCV
    mean_score = grid_search.best_score_  # Best cross-validated accuracy
    std_dev_score = grid_search.cv_results_['std_test_score'][grid_search.best_index_]  # Standard deviation of the best model

    # Print the best parameters and the mean and std of cross-validation
    print(f"Best parameters: {grid_search.best_params_}")
    print(f"Best cross-validated accuracy: {mean_score:.4f}")
    print(f"Standard deviation of cross-validation accuracy: {std_dev_score:.4f}")

    # Use the best estimator from GridSearchCV for evaluation on test data
    best_rf_model = grid_search.best_estimator_

    # Evaluate the best model on the test set
    y_pred_rf = best_rf_model.predict(X_test)
    y_prob_rf = best_rf_model.predict_proba(X_test)[:, 1]  # Probability for class 1 (ASD)

    # Calculate and display the metrics including standard deviation and mean for the test set
    accuracy, specificity, sensitivity, mcc, roc_auc, g_mean = calculate_metrics_with_std(
        y_test, y_pred_rf, y_prob_rf, "Random Forest with Grid Search (10-fold CV)")

    return best_rf_model, mean_score, std_dev_score

# Example usage
best_rf_model, mean_accuracy, std_accuracy = evaluate_grid_search_with_cv(
    X_train_rf, y_train_resampled, X_test_rf, y_test, "Random Forest"
)

print(f"Mean Accuracy from CV: {mean_accuracy:.4f}")
print(f"Standard Deviation from CV: {std_accuracy:.4f}")

Best parameters: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 200}
Best cross-validated accuracy: 0.8955
Standard deviation of cross-validation accuracy: 0.0272
Model: Random Forest with Grid Search (10-fold CV)
Accuracy: 0.8216
MCC: 0.4481
AUC ROC: 0.8351
Sensitivity: 0.6479
Specificity: 0.6479
G Mean: 0.6479
----------------------------------------
Mean Accuracy from CV: 0.8955
Standard Deviation from CV: 0.0272
In [32]:
from sklearn.model_selection import StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (
    accuracy_score, confusion_matrix, matthews_corrcoef,
    roc_auc_score, recall_score
)
import numpy as np

# Function to compute all metrics
def compute_metrics(y_true, y_pred, y_prob):
    conf_matrix = confusion_matrix(y_true, y_pred)
    accuracy = accuracy_score(y_true, y_pred)
    mcc = matthews_corrcoef(y_true, y_pred)
    roc_auc = roc_auc_score(y_true, y_prob)
    sensitivity = recall_score(y_true, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5
    return accuracy, specificity, sensitivity, mcc, roc_auc, g_mean

# Function to evaluate all metrics using manual CV
def evaluate_all_metrics_cv(X, y, model, cv=10):
    skf = StratifiedKFold(n_splits=cv, shuffle=True, random_state=42)

    metrics = {
        'accuracy': [], 'specificity': [], 'sensitivity': [],
        'mcc': [], 'roc_auc': [], 'g_mean': []
    }

    for train_idx, val_idx in skf.split(X, y):
        X_train_fold, X_val_fold = X[train_idx], X[val_idx]
        y_train_fold, y_val_fold = y[train_idx], y[val_idx]

        model.fit(X_train_fold, y_train_fold)
        y_pred = model.predict(X_val_fold)
        y_prob = model.predict_proba(X_val_fold)[:, 1]

        acc, spec, sens, mcc, auc, gmean = compute_metrics(y_val_fold, y_pred, y_prob)
        metrics['accuracy'].append(acc)
        metrics['specificity'].append(spec)
        metrics['sensitivity'].append(sens)
        metrics['mcc'].append(mcc)
        metrics['roc_auc'].append(auc)
        metrics['g_mean'].append(gmean)

    # Print mean and std of each metric
    print("Cross-Validation Metrics (Mean ± Std):")
    for key in metrics:
        mean_val = np.mean(metrics[key])
        std_val = np.std(metrics[key])
        print(f"{key.capitalize()}: {mean_val:.4f} ± {std_val:.4f}")
    
    return metrics

# Example usage with RandomForestClassifier and GridSearch best params
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Sample split if needed
# X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Assuming X_train_rf and y_train_resampled are defined
best_rf = RandomForestClassifier(n_estimators=200, max_depth=20, min_samples_split=5, random_state=42)

# Evaluate metrics with mean and std over CV folds
metrics_cv = evaluate_all_metrics_cv(X_train_rf, y_train_resampled, best_rf, cv=10)

Cross-Validation Metrics (Mean ± Std):
Accuracy: 0.9086 ± 0.0172
Specificity: 0.9669 ± 0.0140
Sensitivity: 0.9669 ± 0.0140
Mcc: 0.8233 ± 0.0333
Roc_auc: 0.9720 ± 0.0065
G_mean: 0.9669 ± 0.0140

CV=10 for all metrices(accuracy,sensitivity, specificity, MCC, AUCROC)¶

In [ ]:
import numpy as np
from sklearn.metrics import accuracy_score, confusion_matrix, matthews_corrcoef, roc_auc_score, recall_score

# Function to calculate performance metrics
def calculate_metrics(y_true, y_pred, y_prob):
    conf_matrix = confusion_matrix(y_true, y_pred)
    accuracy = accuracy_score(y_true, y_pred)
    mcc = matthews_corrcoef(y_true, y_pred)
    roc_auc = roc_auc_score(y_true, y_prob)
    sensitivity = recall_score(y_true, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5
    return accuracy, specificity, sensitivity, mcc, roc_auc, g_mean

# Function to run GridSearchCV and calculate mean and std deviation for all metrics
def evaluate_grid_search_with_cv(X_train, y_train, X_test, y_test, model_name):
    # Define parameter grid
    param_grid = {
        'n_estimators': [100, 200, 300],
        'max_depth': [None, 10, 20, 30],
        'min_samples_split': [2, 5, 10]
    }

    # Initialize Random Forest model
    rf_model = RandomForestClassifier(random_state=42)

    # GridSearchCV for hyperparameter tuning with 10-fold cross-validation
    grid_search = GridSearchCV(estimator=rf_model,
                               param_grid=param_grid,
                               cv=10,  # 10-fold cross-validation
                               scoring='accuracy',
                               return_train_score=True)  # To access train score for each fold

    # Fit the model
    grid_search.fit(X_train, y_train)

    # Extract results from GridSearchCV
    best_model = grid_search.best_estimator_

    # Store metrics for each fold
    all_metrics = {
        'accuracy': [],
        'specificity': [],
        'sensitivity': [],
        'mcc': [],
        'roc_auc': [],
        'g_mean': []
    }

    # Cross-validation loop to get metrics for each fold
    for train_index, val_index in grid_search.cv.split(X_train, y_train):
        X_train_fold, X_val_fold = X_train[train_index], X_train[val_index]
        y_train_fold, y_val_fold = y_train[train_index], y_train[val_index]

        # Train the model on the fold
        best_model.fit(X_train_fold, y_train_fold)

        # Get predictions and probabilities for validation fold
        y_pred_fold = best_model.predict(X_val_fold)
        y_prob_fold = best_model.predict_proba(X_val_fold)[:, 1]  # Probability for class 1 (ASD)

        # Calculate metrics for this fold
        fold_metrics = calculate_metrics(y_val_fold, y_pred_fold, y_prob_fold)

        # Append metrics to the list
        all_metrics['accuracy'].append(fold_metrics[0])
        all_metrics['specificity'].append(fold_metrics[1])
        all_metrics['sensitivity'].append(fold_metrics[2])
        all_metrics['mcc'].append(fold_metrics[3])
        all_metrics['roc_auc'].append(fold_metrics[4])
        all_metrics['g_mean'].append(fold_metrics[5])

    # Compute mean and standard deviation for each metric across all folds
    mean_metrics = {metric: np.mean(values) for metric, values in all_metrics.items()}
    std_metrics = {metric: np.std(values) for metric, values in all_metrics.items()}

    # Print results
    print(f"Model: {model_name}")
    for metric in mean_metrics:
        print(f"{metric.capitalize()} - Mean: {mean_metrics[metric]:.4f}, Std: {std_metrics[metric]:.4f}")

    # Evaluate the best model on the test set
    y_pred_test = best_model.predict(X_test)
    y_prob_test = best_model.predict_proba(X_test)[:, 1]  # Probability for class 1 (ASD)

    # Calculate and display metrics for the test set
    test_metrics = calculate_metrics(y_test, y_pred_test, y_prob_test)
    print(f"\nTest Set Metrics: {model_name}")
    print(f"Accuracy: {test_metrics[0]:.4f}")
    print(f"Sensitivity: {test_metrics[2]:.4f}")
    print(f"Specificity: {test_metrics[1]:.4f}")
    print(f"MCC: {test_metrics[3]:.4f}")
    print(f"AUC ROC: {test_metrics[4]:.4f}")
    print(f"G Mean: {test_metrics[5]:.4f}")
    print("-" * 40)

    return best_model, mean_metrics, std_metrics

# Example usage
best_rf_model, mean_metrics, std_metrics = evaluate_grid_search_with_cv(
    X_train_rf, y_train_resampled, X_test_rf, y_test, "Random Forest"
)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-18-aa28b698d6f0> in <cell line: 100>()
     98 
     99 # Example usage
--> 100 best_rf_model, mean_metrics, std_metrics = evaluate_grid_search_with_cv(
    101     X_train_rf, y_train_resampled, X_test_rf, y_test, "Random Forest"
    102 )

<ipython-input-18-aa28b698d6f0> in evaluate_grid_search_with_cv(X_train, y_train, X_test, y_test, model_name)
     49 
     50     # Cross-validation loop to get metrics for each fold
---> 51     for train_index, val_index in grid_search.cv.split(X_train, y_train):
     52         X_train_fold, X_val_fold = X_train[train_index], X_train[val_index]
     53         y_train_fold, y_val_fold = y_train[train_index], y_train[val_index]

AttributeError: 'int' object has no attribute 'split'
In [ ]:
import matplotlib.pyplot as plt
import pandas as pd

# Sample data based on the provided text
data = {
    'Resampling': [
        'RandomOverSampler', 'RandomOverSampler', 'RandomOverSampler', 'RandomOverSampler',
        'SMOTE', 'SMOTE', 'SMOTE', 'SMOTE',
        'ADASYN', 'ADASYN', 'ADASYN', 'ADASYN'
    ],
    'Feature_Selection': [
        'Full Feature Set', 'Lasso', 'Random Forest', 'Best-First Search',
        'Full Feature Set', 'Lasso', 'Random Forest', 'Best-First Search',
        'Full Feature Set', 'Lasso', 'Random Forest', 'Best-First Search'
    ],
    'Accuracy': [0.8521, 0.8333, 0.8568, 0.8333, 0.8028, 0.8239, 0.8028, 0.7864, 0.8146, 0.8099, 0.8099, 0.7911],
    'MCC': [0.3984, 0.2958, 0.4527, 0.3761, 0.3663, 0.4253, 0.3663, 0.3468, 0.3799, 0.3786, 0.4000, 0.3837],
    'AUC_ROC': [0.8462, 0.8201, 0.8435, 0.8286, 0.8299, 0.8248, 0.8336, 0.8157, 0.8260, 0.8175, 0.8338, 0.8060],
    'Sensitivity': [0.3944, 0.2958, 0.4930, 0.4507, 0.5493, 0.5915, 0.5493, 0.5634, 0.5352, 0.5493, 0.5915, 0.6197],
    'Specificity': [0.3944, 0.2958, 0.4930, 0.4507, 0.5493, 0.5915, 0.5493, 0.5634, 0.5352, 0.5493, 0.5915, 0.6197],
    'G_Mean': [0.3944, 0.2958, 0.4930, 0.4507, 0.5493, 0.5915, 0.5493, 0.5634, 0.5352, 0.5493, 0.5915, 0.6197]
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Set up the plot for each metric
metrics = ['Accuracy', 'MCC', 'AUC_ROC', 'Sensitivity', 'Specificity', 'G_Mean']
feature_selections = df['Feature_Selection'].unique()
resampling_methods = df['Resampling'].unique()

# Plot each metric in a separate figure
for metric in metrics:
    plt.figure(figsize=(10, 6))
    for resampling in resampling_methods:
        subset = df[df['Resampling'] == resampling]
        plt.bar(subset['Feature_Selection'] + ' - ' + resampling, subset[metric], label=resampling)

    # Add title and labels
    plt.title(f'{metric} Comparison Across Feature Selection Methods and Resampling')
    plt.xlabel('Feature Selection Method')
    plt.ylabel(metric)
    plt.xticks(rotation=45, ha='right')
    plt.legend(title="Resampling Method")
    plt.tight_layout()

# Show the plots
plt.show()
In [ ]:
import matplotlib.pyplot as plt

# Feature selection techniques and their respective accuracies with Random OverSampler
techniques = ['Full Feature Set', 'Lasso', 'Random Forest', 'Best-First Search']
accuracies = [0.8232, 0.8333, 0.8568, 0.8333]

# Plotting the bar chart
plt.figure(figsize=(5, 6))
plt.bar(techniques, accuracies, color=['blue', 'orange', 'green', 'red'], width=0.4)

# Adding labels and title
plt.xlabel('Feature Selection Techniques')
plt.ylabel('Accuracy')
plt.title('Accuracy of Different Feature Selection Techniques with Random OverSampler')
plt.ylim(0.8, 0.9)  # Setting y-axis limit for better visualization
plt.xticks(rotation=15)
# plt.grid(axis='y', linestyle='--', alpha=0.1)

plt.show()
In [ ]:
import matplotlib.pyplot as plt
import pandas as pd

# Sample data based on your output, with columns for each metric
data = {
    'Resampling': [
        'RandomOverSampler', 'RandomOverSampler', 'RandomOverSampler', 'RandomOverSampler',
        'SMOTE', 'SMOTE', 'SMOTE', 'SMOTE',
        'ADASYN', 'ADASYN', 'ADASYN', 'ADASYN'
    ],
    'Feature_Selection': [
        'Full Feature Set', 'Lasso', 'Random Forest', 'Best-First Search',
        'Full Feature Set', 'Lasso', 'Random Forest', 'Best-First Search',
        'Full Feature Set', 'Lasso', 'Random Forest', 'Best-First Search'
    ],
    'Accuracy': [0.8521, 0.8333, 0.8568, 0.8333, 0.8028, 0.8239, 0.8028, 0.7864, 0.8146, 0.8099, 0.8099, 0.7911],
    'MCC': [0.3984, 0.2958, 0.4527, 0.3761, 0.3663, 0.4253, 0.3663, 0.3468, 0.3799, 0.3786, 0.4000, 0.3837],
    'AUC_ROC': [0.8462, 0.8201, 0.8435, 0.8286, 0.8299, 0.8248, 0.8336, 0.8157, 0.8260, 0.8175, 0.8338, 0.8060],
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Calculate an "average score" across the key metrics for each combination of resampling and feature selection
df['Average_Score'] = df[['Accuracy', 'MCC', 'AUC_ROC']].mean(axis=1)

# Plot average scores to identify the best-performing combination
fig, ax = plt.subplots(figsize=(10, 6))

# Use a bar plot where feature selections are grouped and resampling techniques are compared within each group
for idx, feature_selection in enumerate(df['Feature_Selection'].unique()):
    subset = df[df['Feature_Selection'] == feature_selection]
    ax.bar([idx*3 + i for i in range(len(subset))], subset['Average_Score'], label=subset['Resampling'].values)

# Set labels and title
ax.set_xticks([i*3+1 for i in range(len(df['Feature_Selection'].unique()))])
ax.set_xticklabels(df['Feature_Selection'].unique(), rotation=45)
ax.set_ylabel('Average Score (Accuracy, MCC, AUC ROC)')
ax.set_title('Comparison of Feature Selection and Resampling Techniques')
ax.legend(title="Resampling Method")

# Display plot
plt.tight_layout()
plt.show()
In [ ]:
        # Create a LIME explainer with trimmed feature names
        explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

        # Explain a prediction
        for i in range(10):  # Explain the first 10 instances
            exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)
            exp.show_in_notebook(show_all=False)
            print(f"Gene being explained: {genes_test.iloc[i]}")

            # Show LIME prediction and compare with actual label
            lime_prediction = model.predict([X_test_fs[i]])[0]
            actual_label = y_test.iloc[i]
            print(f"LIME Prediction: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual Label: {'ASD' if actual_label == 1 else 'Non-ASD'}")
            print(f"Prediction Match: {lime_prediction == actual_label}")
            print("-" * 40)
In [ ]:
In [ ]:
# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)
    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Evaluate the model
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = RandomForestClassifier(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Sampling techniques to apply
sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']

for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train, y_train, technique)

    # Apply feature selection methods
    X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)
    X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)

    # Apply PCA
    X_train_pca, X_test_pca = fit_pca(X_train_resampled, X_test)
    pca_feature_names = [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]

    # Define models with trimmed feature names
    models = {
        "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
        "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
        # "PCA": (X_train_pca, X_test_pca, pca_feature_names),
        # "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
    }

    for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
        model = evaluate_model(X_train_fs, X_test_fs, y_train_resampled, y_test, name, fs_feature_names)

        # Create a LIME explainer with trimmed feature names
        explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

        # Explain a prediction
        for i in range(10):  # Explain the first 3 instances
            exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)
            exp.show_in_notebook(show_all=False)
            print(f"Gene being explained: {genes_test.iloc[i]}")

            # Show LIME prediction and compare with actual label
            lime_prediction = model.predict([X_test_fs[i]])[0]
            actual_label = y_test.iloc[i]
            print(f"LIME Prediction: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual Label: {'ASD' if actual_label == 1 else 'Non-ASD'}")
            print(f"Prediction Match: {lime_prediction == actual_label}")
            print("-" * 40)
In [ ]:
# Import necessary libraries
import matplotlib.pyplot as plt
import lime
import lime.lime_tabular

# Function to plot LIME explanation for ASD class
def plot_lime_explanation_for_asd(instance_index, X_test, model, feature_names, class_names=['Non-ASD', 'ASD']):
    # Create a LIME explainer with the feature names
    explainer = lime.lime_tabular.LimeTabularExplainer(
        training_data=X_test,
        feature_names=feature_names,
        class_names=class_names,
        discretize_continuous=True
    )

    # Explain a specific instance
    exp = explainer.explain_instance(X_test[instance_index], model.predict_proba, num_features=10)

    # Display the explanation
    exp.as_pyplot_figure(label=1)  # Label=1 for the ASD class
    plt.title(f'LIME Explanation for Class ASD (Instance {instance_index})')
    plt.show()

# Example usage
instance_to_explain = 5  # Change this to the index of the instance you want to explain
plot_lime_explanation_for_asd(instance_to_explain, X_test_fs, model, fs_feature_names)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-17-3d7df2c3c2bb> in <cell line: 26>()
     24 # Example usage
     25 instance_to_explain = 5  # Change this to the index of the instance you want to explain
---> 26 plot_lime_explanation_for_asd(instance_to_explain, X_test_fs, model, fs_feature_names)

NameError: name 'X_test_fs' is not defined
In [ ]:
# Assuming `exp` is the LIME explanation object for an instance
exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)

# Initialize sums
positive_contribution = 0
negative_contribution = 0

# Loop through the explained features and their contributions
for feature, contribution in exp.as_list():
    if contribution > 0:
        positive_contribution += contribution  # Pushes towards ASD (green bars)
    else:
        negative_contribution += contribution  # Pushes away from ASD (red bars)

# Compare cumulative contributions
print(f"Positive (ASD) Contribution: {positive_contribution:.4f}")
print(f"Negative (Non-ASD) Contribution: {negative_contribution:.4f}")

# Determine prediction tendency
if positive_contribution > abs(negative_contribution):
    print("Model prediction is leaning towards ASD.")
else:
    print("Model prediction is leaning towards Non-ASD.")

Positive (ASD) Contribution: 0.2396
Negative (Non-ASD) Contribution: -0.4241
Model prediction is leaning towards Non-ASD.
In [ ]:
# Function to extract the sex information from feature names
def extract_sex_from_feature(name):
    # Return 1 for 'M' (male) and 0 for 'F' (female) based on the feature name
    return 1 if 'M_' in name else 0

# Create a DataFrame for sex feature
sex_feature = pd.Series([extract_sex_from_feature(name) for name in X.columns], name='Sex')

# Add the sex feature to the dataset
X_with_sex = X.copy()
X_with_sex['Sex'] = sex_feature

# Handle missing values (if any) by imputation
from sklearn.impute import SimpleImputer

# Create an imputer object with a strategy to fill missing values (e.g., mean)
imputer = SimpleImputer(strategy='mean')

# Impute missing values
X_with_sex_imputed = imputer.fit_transform(X_with_sex)

# Create a DataFrame from the imputed data
X_with_sex_imputed = pd.DataFrame(X_with_sex_imputed, columns=X_with_sex.columns)

# Split the updated dataset
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X_with_sex_imputed, Y, genes, test_size=0.2, random_state=42)

# Apply sampling techniques and feature selection methods
for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train, y_train, technique)

    # Apply feature selection methods
    X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train_resampled, y_train_resampled, X_test, X_with_sex_imputed.columns)
    X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, X_with_sex_imputed.columns)

    # Apply PCA
    X_train_pca, X_test_pca = fit_pca(X_train_resampled, X_test)
    pca_feature_names = [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]

    # Define models with updated feature names
    models = {
        "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
        "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
        # "PCA": (X_train_pca, X_test_pca, pca_feature_names),
        # "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
    }

    for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
        model = evaluate_model(X_train_fs, X_test_fs, y_train_resampled, y_test, name, fs_feature_names)

        # Create a LIME explainer with updated feature names
        explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

        # Explain a prediction
        for i in range(3):  # Explain the first 3 instances
            exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)
            exp.show_in_notebook(show_all=False)
            print(f"Gene being explained: {genes_test.iloc[i]}")

            # Show LIME prediction and compare with actual label
            lime_prediction = model.predict([X_test_fs[i]])[0]
            actual_label = y_test.iloc[i]
            print(f"LIME Prediction: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual Label: {'ASD' if actual_label == 1 else 'Non-ASD'}")
            print(f"Prediction Match: {lime_prediction == actual_label}")
            print("-" * 40)

Applying SHAP¶

In [ ]:
!pip install shap

Requirement already satisfied: shap in /usr/local/lib/python3.10/dist-packages (0.46.0)
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In [ ]:
import shap
import matplotlib.pyplot as plt

# load JS visualization code to notebook
shap.initjs()

# Create the explainer
explainer = shap.TreeExplainer(model)

shap_values = explainer.shap_values(X)
In [ ]:
import shap
import matplotlib.pyplot as plt

print("Variable Importance Plot - Global Interpretation")
print("SHAP values shape:", shap_values.shape)
print("X shape:", X.shape)

# For binary classification, use the SHAP values for one class
shap_values_class1 = shap_values[:, :, 1]  # Assuming shape (num_samples, num_features, num_classes)

# Create the summary plot
plt.figure(figsize=(10, 8))
shap.summary_plot(shap_values_class1, X)
plt.tight_layout()
plt.show()

Variable Importance Plot - Global Interpretation
SHAP values shape: (2128, 524, 2)
X shape: (2128, 524)

<Figure size 640x480 with 0 Axes>
In [ ]:
shap.initjs()
In [ ]:
shap.force_plot(explainer.expected_value[0], shap_values[0, :, 0], X.iloc[0], matplotlib=True)
In [ ]:
print("Variable Importance Plot - Global Interpretation")
figure = plt.figure()
shap.summary_plot(shap_values, X_test)
In [ ]:
# Save the DataFrame with the Sex feature to a CSV file
X_with_sex.to_csv('/content/X_with_sex.csv', index=False)
In [ ]:
# Load the saved CSV file
X_with_sex_loaded = pd.read_csv('/content/X_with_sex.csv')

# Display the first few rows to check the gender column
print(X_with_sex_loaded.head())

   Ocx_8pcw_M_13058  M1C_S1C_8pcw_M_13058  AMY_8pcw_M_13058  MGE_8pcw_M_13058  \
0              4.93                  5.15              5.43              6.00   
1              2.86                  2.87              3.93              3.23   
2              1.46                  0.95              1.19              1.46   
3              3.81                  3.32              2.95              3.49   
4              5.04                  5.49              5.31              5.32   

   STC_8pcw_M_13058  URL_8pcw_M_13058  CGE_8pcw_M_13058  DTH_8pcw_M_13058  \
0              4.83              5.59              5.49              5.23   
1              3.54              3.84              2.84              4.15   
2              1.25              0.78              1.16              0.73   
3              3.12              2.56              3.44              2.87   
4              5.47              5.20              5.40              5.03   

   MFC_8pcw_M_13058  DFC_8pcw_M_13058  ...  CBC_40yrs_F_12304  \
0              4.65              4.59  ...               4.81   
1              2.91              3.11  ...               2.95   
2              1.38              1.57  ...               0.44   
3              3.71              3.92  ...               2.71   
4              4.85              4.49  ...               3.31   

   V1C_40yrs_F_12304  OFC_40yrs_F_12304  STC_40yrs_F_12304  IPC_40yrs_F_12304  \
0               6.02               6.16               6.15               6.18   
1               3.41               4.55               4.22               4.49   
2               0.88               0.41               0.44               0.52   
3               2.90               2.93               2.93               3.13   
4               3.56               3.65               3.59               3.73   

   M1C_40yrs_F_12304  HIP_40yrs_F_12304  STR_40yrs_F_12304  S1C_40yrs_F_12304  \
0               5.28               5.06               6.30               5.71   
1               2.90               4.17               3.30               3.56   
2               0.69               1.02               1.33               0.77   
3               3.34               3.41               3.30               3.38   
4               2.82               3.30               3.15               2.96   

   Sex  
0  1.0  
1  1.0  
2  1.0  
3  1.0  
4  1.0  

[5 rows x 525 columns]
In [ ]:
# List all column names in the DataFrame
print(X_with_sex_loaded.columns)

Index(['Ocx_8pcw_M_13058', 'M1C_S1C_8pcw_M_13058', 'AMY_8pcw_M_13058',
       'MGE_8pcw_M_13058', 'STC_8pcw_M_13058', 'URL_8pcw_M_13058',
       'CGE_8pcw_M_13058', 'DTH_8pcw_M_13058', 'MFC_8pcw_M_13058',
       'DFC_8pcw_M_13058',
       ...
       'CBC_40yrs_F_12304', 'V1C_40yrs_F_12304', 'OFC_40yrs_F_12304',
       'STC_40yrs_F_12304', 'IPC_40yrs_F_12304', 'M1C_40yrs_F_12304',
       'HIP_40yrs_F_12304', 'STR_40yrs_F_12304', 'S1C_40yrs_F_12304', 'Sex'],
      dtype='object', length=525)
In [ ]:
import pandas as pd

# Load the feature dataset (with features but without target variable)
features_file_path = '/content/X_with_sex.csv'
features_df = pd.read_csv(features_file_path)

# Load the target variable dataset (assuming it has the same index or identifier as the feature dataset)
# target_file_path = '/content/Y_with_target.csv'  # Update this path to your actual target file
# target_df = pd.read_csv(target_file_path)

# # Ensure the target dataset contains the 'Gene Type' column
# print(target_df.head())

# Merge the datasets on a common identifier if needed (e.g., 'ID' or index)
# For this example, I'm assuming the index is the common key
# Make sure 'ID' or index is the same in both datasets or adapt the merge key accordingly
merged_df = pd.concat([features_df, Y], axis=1)

# Verify the merged dataset
print(merged_df.head())

   Ocx_8pcw_M_13058  M1C_S1C_8pcw_M_13058  AMY_8pcw_M_13058  MGE_8pcw_M_13058  \
0              4.93                  5.15              5.43              6.00   
1              2.86                  2.87              3.93              3.23   
2              1.46                  0.95              1.19              1.46   
3              3.81                  3.32              2.95              3.49   
4              5.04                  5.49              5.31              5.32   

   STC_8pcw_M_13058  URL_8pcw_M_13058  CGE_8pcw_M_13058  DTH_8pcw_M_13058  \
0              4.83              5.59              5.49              5.23   
1              3.54              3.84              2.84              4.15   
2              1.25              0.78              1.16              0.73   
3              3.12              2.56              3.44              2.87   
4              5.47              5.20              5.40              5.03   

   MFC_8pcw_M_13058  DFC_8pcw_M_13058  ...  V1C_40yrs_F_12304  \
0              4.65              4.59  ...               6.02   
1              2.91              3.11  ...               3.41   
2              1.38              1.57  ...               0.88   
3              3.71              3.92  ...               2.90   
4              4.85              4.49  ...               3.56   

   OFC_40yrs_F_12304  STC_40yrs_F_12304  IPC_40yrs_F_12304  M1C_40yrs_F_12304  \
0               6.16               6.15               6.18               5.28   
1               4.55               4.22               4.49               2.90   
2               0.41               0.44               0.52               0.69   
3               2.93               2.93               3.13               3.34   
4               3.65               3.59               3.73               2.82   

   HIP_40yrs_F_12304  STR_40yrs_F_12304  S1C_40yrs_F_12304  Sex  Gene Type  
0               5.06               6.30               5.71  1.0          1  
1               4.17               3.30               3.56  1.0          1  
2               1.02               1.33               0.77  1.0          1  
3               3.41               3.30               3.38  1.0          1  
4               3.30               3.15               2.96  1.0          1  

[5 rows x 526 columns]
In [ ]:
# Map 'Sex' values to categorical
merged_df['Sex'] = merged_df['Sex'].map({1: 'Male', 0: 'Female'})

# Calculate the counts of males and females for ASD and Non-ASD
gender_asd_counts = merged_df[merged_df['Gene Type'] == 'ASD'].groupby('Sex').size()
gender_non_asd_counts = merged_df[merged_df['Gene Type'] == 'Non-ASD'].groupby('Sex').size()

# Print out the counts
print("Counts of Males and Females associated with ASD:")
print(gender_asd_counts)
print("\nCounts of Males and Females associated with Non-ASD:")
print(gender_non_asd_counts)

# Calculate and display proportions
total_asd = gender_asd_counts.sum()
total_non_asd = gender_non_asd_counts.sum()

print("\nProportion of Males and Females associated with ASD:")
print(f"Male: {gender_asd_counts.get('Male', 0)} ({(gender_asd_counts.get('Male', 0) / total_asd) * 100:.2f}%)")
print(f"Female: {gender_asd_counts.get('Female', 0)} ({(gender_asd_counts.get('Female', 0) / total_asd) * 100:.2f}%)")

print("\nProportion of Males and Females associated with Non-ASD:")
print(f"Male: {gender_non_asd_counts.get('Male', 0)} ({(gender_non_asd_counts.get('Male', 0) / total_non_asd) * 100:.2f}%)")
print(f"Female: {gender_non_asd_counts.get('Female', 0)} ({(gender_non_asd_counts.get('Female', 0) / total_non_asd) * 100:.2f}%)")

Counts of Males and Females associated with ASD:
Series([], dtype: int64)

Counts of Males and Females associated with Non-ASD:
Series([], dtype: int64)

Proportion of Males and Females associated with ASD:
Male: 0 (nan%)
Female: 0 (nan%)

Proportion of Males and Females associated with Non-ASD:
Male: 0 (nan%)
Female: 0 (nan%)

<ipython-input-28-ed4b5507f0ae>:19: RuntimeWarning: invalid value encountered in scalar divide
  print(f"Male: {gender_asd_counts.get('Male', 0)} ({(gender_asd_counts.get('Male', 0) / total_asd) * 100:.2f}%)")
<ipython-input-28-ed4b5507f0ae>:20: RuntimeWarning: invalid value encountered in scalar divide
  print(f"Female: {gender_asd_counts.get('Female', 0)} ({(gender_asd_counts.get('Female', 0) / total_asd) * 100:.2f}%)")
<ipython-input-28-ed4b5507f0ae>:23: RuntimeWarning: invalid value encountered in scalar divide
  print(f"Male: {gender_non_asd_counts.get('Male', 0)} ({(gender_non_asd_counts.get('Male', 0) / total_non_asd) * 100:.2f}%)")
<ipython-input-28-ed4b5507f0ae>:24: RuntimeWarning: invalid value encountered in scalar divide
  print(f"Female: {gender_non_asd_counts.get('Female', 0)} ({(gender_non_asd_counts.get('Female', 0) / total_non_asd) * 100:.2f}%)")
In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN
from lime import lime_tabular

# Load the dataset
file_path = '/content/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Save feature names for later
feature_names = X.columns

# Scale the features
# scaler = StandardScaler()
# X_scaled = scaler.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X, Y, genes, test_size=0.2, random_state=42)

# Define sampling techniques
def apply_sampling(X_train, y_train, technique):
    if technique == 'RandomOverSampler':
        sampler = RandomOverSampler(random_state=42)
    elif technique == 'SMOTE':
        sampler = SMOTE(random_state=42)
    elif technique == 'ADASYN':
        sampler = ADASYN(random_state=42)
    else:
        raise ValueError("Unknown sampling technique")

    X_train_resampled, y_train_resampled = sampler.fit_resample(X_train, y_train)
    return X_train_resampled, y_train_resampled

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), feature_names[selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), feature_names[selected_indices]

# # Define PCA transformation
# def apply_pca(X_train, X_test):
#     pca = PCA(n_components=0.95)  # Keep 95% of the variance
#     X_train_pca = pca.fit_transform(X_train)
#     X_test_pca = pca.transform(X_test)
#     return X_train_pca, X_test_pca
# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)
    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Fit PCA and plot explained variance
# X_train_pca, X_test_pca = fit_pca(X_train_resampled, X_test)
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = LogisticRegression(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Function to train and evaluate a model, and visualize the confusion matrix
# def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
#     model = LogisticRegression(random_state=42)
#     # model = RandomForestClassifier(random_state=42)
#     model.fit(X_train, y_train)
#     y_pred = model.predict(X_test)
#     y_prob = model.predict_proba(X_test)[:, 1]

#     conf_matrix = confusion_matrix(y_test, y_pred)
#     accuracy = accuracy_score(y_test, y_pred)
#     mcc = matthews_corrcoef(y_test, y_pred)
#     roc_auc = roc_auc_score(y_test, y_prob)
#     f1 = f1_score(y_test, y_pred)
#     sensitivity = recall_score(y_test, y_pred)
#     specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
#     g_mean = (sensitivity * specificity) ** 0.5

#     print(f"Model: {model_name}")
#     print(f"Accuracy: {accuracy:.4f}")
#     print(f"MCC: {mcc:.4f}")
#     print(f"AUC ROC: {roc_auc:.4f}")
#     print(f"F1 Score: {f1:.4f}")
#     print(f"Sensitivity: {sensitivity:.4f}")
#     print(f"Specificity: {specificity:.4f}")
#     print(f"G Mean: {g_mean:.4f}")
#     print("-" * 40)

#     # Visualize the confusion matrix
#     plt.figure(figsize=(8, 6))
#     sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
#     plt.title(f'Confusion Matrix - {model_name}')
#     plt.xlabel('Predicted Label')
#     plt.ylabel('True Label')
#     plt.show()

#     return model

# Sampling techniques to apply
sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']

for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train, y_train, technique)

    # Apply feature selection methods
    X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train_resampled, y_train_resampled, X_test, feature_names)
    X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, feature_names)

    # Apply PCA
    # X_train_pca, X_test_pca = apply_pca(X_train_resampled, X_test)
    X_train_pca, X_test_pca = fit_pca(X_train_resampled, X_test)
    # Train and evaluate models with different feature selections
    models = {
        "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
        "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
        # "PCA": (X_train_pca, X_test_pca, [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]),
        # "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
    }


    for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
        model = evaluate_model(X_train_fs, X_test_fs, y_train_resampled, y_test, name, fs_feature_names)

        # Create a LIME explainer
        explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

        # # Explain a prediction
        for i in range(3):  # Explain the first 3 instances
            exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)
            exp.show_in_notebook(show_all=False)
            # plot_lime_explanation(exp, f"LIME explanation for {genes_test.iloc[i]}", figsize=(20, 10))
            print(f"Gene being explained: {genes_test.iloc[i]}")

            # Show LIME prediction and compare with actual label
            lime_prediction = model.predict([X_test_fs[i]])[0]
            actual_label = y_test.iloc[i]
            print(f"LIME Prediction: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual Label: {'ASD' if actual_label == 1 else 'Non-ASD'}")
            print(f"Prediction Match: {lime_prediction == actual_label}")
            print("-" * 40)
In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN
from lime import lime_tabular

# Load the dataset
file_path = '/content/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Identify male and female feature columns based on the pattern
male_features = [col for col in X.columns if '_M_' in col]
female_features = [col for col in X.columns if '_F_' in col]

# Create separate DataFrames for male and female features
X_male = X[male_features]
X_female = X[female_features]

# Count how many males are diagnosed with ASD
male_asd_count = df[df['Gene Type'] == 'ASD'][male_features].sum().sum()
print(f"Number of males diagnosed with ASD: {male_asd_count}")

# Optional: Combine male and female features if needed
X_combined = pd.concat([X_male, X_female], axis=1)

# Save feature names for later
feature_names = X_combined.columns

# The rest of your code continues here...


# The rest of the code follows here...


# Scale the features
# scaler = StandardScaler()
# X_scaled = scaler.fit_transform(X_combined)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X_combined, Y, genes, test_size=0.2, random_state=42)

# Define sampling techniques
def apply_sampling(X_train, y_train, technique):
    if technique == 'RandomOverSampler':
        sampler = RandomOverSampler(random_state=42)
    elif technique == 'SMOTE':
        sampler = SMOTE(random_state=42)
    elif technique == 'ADASYN':
        sampler = ADASYN(random_state=42)
    else:
        raise ValueError("Unknown sampling technique")

    X_train_resampled, y_train_resampled = sampler.fit_resample(X_train, y_train)
    return X_train_resampled, y_train_resampled

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), feature_names[selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), feature_names[selected_indices]

# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)
    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Function to evaluate model performance
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = LogisticRegression(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Sampling techniques to apply
sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']

for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train, y_train, technique)

    # Apply feature selection methods
    X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train_resampled, y_train_resampled, X_test, feature_names)
    X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, feature_names)

    # Apply PCA
    X_train_pca, X_test_pca = fit_pca(X_train_resampled, X_test)

    # Train and evaluate models with different feature selections
    models = {
        "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
        "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
    }

    for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
        model = evaluate_model(X_train_fs, X_test_fs, y_train_resampled, y_test, name, fs_feature_names)

        # Create a LIME explainer
        explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

        # Explain a prediction
        for i in range(3):  # Explain the first 3 instances
            exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)
            exp.show_in_notebook(show_all=False)
            print(f"Gene being explained: {genes_test.iloc[i]}")

            # Show LIME prediction and compare with actual label
            lime_prediction = model.predict([X_test_fs[i]])[0]
            actual_label = y_test.iloc[i]
            print(f"LIME Prediction: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual Label: {'ASD' if actual_label == 1 else 'Non-ASD'}")
            print(f"Prediction Match: {lime_prediction == actual_label}")
            print("-" * 40)
In [ ]:
import pandas as pd

# Load the dataset
file_path = '/content/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Define the function to extract gender from feature names
def extract_gender(feature_name):
    if isinstance(feature_name, str):
        if 'M' in feature_name:
            return 1
        elif 'F' in feature_name:
            return 0
    return None

# Separate features (X) and target variable (Y)
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)

# Create gender columns based on feature names in X
gender_columns = [col for col in X.columns if '_F_' in col or '_M_' in col]

# Create a dictionary to hold new gender columns
gender_data = {}
for col in gender_columns:
    gender_data[col + '_Gender'] = X[col].apply(lambda x: extract_gender(x) if pd.notnull(x) else None)

# Create a new DataFrame from the dictionary
gender_df = pd.DataFrame(gender_data)

# Concatenate the new DataFrame with the existing one
df = pd.concat([df, gender_df], axis=1)

# Encode target variable 'Gene Type'
df['Gene Type'] = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)

# Save the updated DataFrame
df.to_csv('/content/Updated_Dataset.csv', index=False)

# Analyze if males are more associated with ASD for all gender columns
gender_columns = [col for col in df.columns if '_Gender' in col]

for gender_column in gender_columns:
    if gender_column in df.columns:
        print(f"\nAnalyzing column: {gender_column}")

        # Calculate average ASD association by gender
        gender_asd_correlation = df.groupby(gender_column)['Gene Type'].mean()
        print("Average ASD association by gender:")
        print(gender_asd_correlation)

        # Calculate proportion of ASD among males and females
        male_asd_proportion = df[df[gender_column] == 1]['Gene Type'].mean()
        female_asd_proportion = df[df[gender_column] == 0]['Gene Type'].mean()
        print(f"Proportion of ASD among males: {male_asd_proportion:.2f}")
        print(f"Proportion of ASD among females: {female_asd_proportion:.2f}")
    else:
        print(f"Gender column '{gender_column}' not found in DataFrame.")

Analyzing column: Ocx_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_S1C_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MGE_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: URL_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CGE_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DTH_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: LGE_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: PCx_8pcw_M_13058_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DTH_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: URL_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: LGE_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_S1C_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MGE_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: TCx_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: Ocx_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CGE_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: PCx_9pcw_M_12833_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DTH_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_12pcw_F_12835_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CB_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DTH_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_12pcw_F_12960_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DTH_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_12pcw_F_13060_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_13pcw_M_12820_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CB_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_13pcw_F_12834_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CB_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_13pcw_M_12888_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_16pcw_M_12287_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_16pcw_M_12837_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_S1C_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_16pcw_M_12879_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_S1C_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_17pcw_F_12880_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_S1C_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_19pcw_F_12885_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_21pcw_F_12365_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_21pcw_F_12365_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_21pcw_M_12886_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_24pcw_M_12288_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_25pcw_F_12948_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_26pcw_F_12949_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_26pcw_F_12949_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_26pcw_F_12949_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_35pcw_F_12295_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_35pcw_F_12295_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_37pcw_M_263195015_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_4mos_M_12296_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_4mos_M_12889_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_4mos_M_12890_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_10mos_M_12977_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_1yrs_F_12830_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_2yrs_F_12979_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_3yrs_F_12836_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_3yrs_M_12980_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_4yrs_M_12298_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_4yrs_M_12298_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_4yrs_M_12298_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_4yrs_M_12298_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_4yrs_M_12298_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_4yrs_M_12298_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_4yrs_M_12298_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_8yrs_M_12841_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_8yrs_M_12981_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_11yrs_F_12289_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_13yrs_F_12831_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_15yrs_M_12299_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_15yrs_M_12299_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_15yrs_M_12299_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_15yrs_M_12299_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_15yrs_M_12299_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_18yrs_M_12984_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_19yrs_F_12832_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_21yrs_F_13057_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_23yrs_M_12300_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_30yrs_F_12290_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_36yrs_M_12302_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MFC_37yrs_M_12303_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: DFC_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: ITC_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: VFC_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: MD_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: AMY_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: A1C_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: CBC_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: V1C_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: OFC_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STC_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: IPC_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: M1C_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: HIP_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: STR_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan

Analyzing column: S1C_40yrs_F_12304_Gender
Average ASD association by gender:
Series([], Name: Gene Type, dtype: float64)
Proportion of ASD among males: nan
Proportion of ASD among females: nan
In [ ]:
def extract_gender(feature_name):
    if 'F_' in feature_name:
        return 'Female'
    elif 'M_' in feature_name:
        return 'Male'
    else:
        return 'Unknown'  # Handle cases where gender might not be identified

# Apply the function to create a Gender column in a DataFrame
gender_df = pd.DataFrame({
    'feature_names': X.columns,
    'Gender': [extract_gender(name) for name in X.columns]
})

print(gender_df.head(10))

          feature_names Gender
0      Ocx_8pcw_M_13058   Male
1  M1C_S1C_8pcw_M_13058   Male
2      AMY_8pcw_M_13058   Male
3      MGE_8pcw_M_13058   Male
4      STC_8pcw_M_13058   Male
5      URL_8pcw_M_13058   Male
6      CGE_8pcw_M_13058   Male
7      DTH_8pcw_M_13058   Male
8      MFC_8pcw_M_13058   Male
9      DFC_8pcw_M_13058   Male
In [ ]:
# Check for duplicate feature names
duplicates = df_features.columns[df_features.columns.duplicated()]

if not duplicates.empty:
    print("Duplicate feature names found:")
    print(duplicates)

Duplicate feature names found:
Index(['Gender', 'Gender'], dtype='object')
In [ ]:
# Create a DataFrame with unique feature names and corresponding genders
gender_df_unique = gender_df.drop_duplicates(subset='Feature Name')

# Create a dictionary mapping feature names to gender
gender_mapping = dict(zip(gender_df_unique['Feature Name'], gender_df_unique['Gender']))
In [ ]:
# Ensure gender_mapping only contains features present in df_features
gender_mapping = {feature: gender for feature, gender in zip(gender_df_unique['Feature Name'], gender_df_unique['Gender']) if feature in df_features.columns}
In [ ]:
# Check for columns in df_features that are in the gender_mapping
common_features = [feature for feature in df_features.columns if feature in gender_mapping]

# Check for the length of common features and df_features
print(f"Length of df_features columns: {len(df_features.columns)}")
print(f"Length of common features: {len(common_features)}")

# Create a DataFrame with only the common features
df_features_common = df_features[common_features]

# Add the Gender column based on the mapping
df['Gender'] = df_features_common.columns.to_series().map(gender_mapping)

Length of df_features columns: 525
Length of common features: 524
In [ ]:
# Identify feature names in df_features that do not have a corresponding gender mapping
missing_mappings = [feature for feature in df_features.columns if feature not in gender_mapping]
print("Features with missing gender mappings:")
print(missing_mappings)

Features with missing gender mappings:
['Gender']
In [ ]:
# Verify the Gender column
print(df[['Gene', 'Gender']].head())

    Gene Gender Gender Gender
0   ABAT    NaN    NaN    NaN
1  ACSL4    NaN    NaN    NaN
2    ADA    NaN    NaN    NaN
3    ADK    NaN    NaN    NaN
4   ADNP    NaN    NaN    NaN
In [ ]:
# Check if the Gender column was added correctly
print(df[['Gene', 'Gender']].head())

    Gene Gender Gender Gender
0   ABAT    NaN    NaN    NaN
1  ACSL4    NaN    NaN    NaN
2    ADA    NaN    NaN    NaN
3    ADK    NaN    NaN    NaN
4   ADNP    NaN    NaN    NaN
In [ ]:
# First, ensure that the 'Feature' column in gender_df matches the corresponding column in df

# Extract gender from the feature names in df
df['Gender'] = gender_df['feature_names'].apply(lambda x: extract_gender(x))

# Check if 'Gender' has been added correctly
print(df[['Gene', 'Gender', 'Gene Type']].head(10))

      Gene Gender Gender Gender Gene Type
0     ABAT   Male   Male   Male       ASD
1    ACSL4   Male   Male   Male       ASD
2      ADA   Male   Male   Male       ASD
3      ADK   Male   Male   Male       ASD
4     ADNP   Male   Male   Male       ASD
5  ADORA2A   Male   Male   Male       ASD
6    ADRB2   Male   Male   Male       ASD
7     ADSL   Male   Male   Male       ASD
8     AFF2   Male   Male   Male       ASD
9    AGAP1   Male   Male   Male       ASD
In [ ]:
# Group by 'Gender' and 'Gene Type' to see the distribution
gender_distribution = df.groupby(['Gender', 'Gene Type']).size().unstack(fill_value=0)

print(gender_distribution)

# Plot the distribution
gender_distribution.plot(kind='bar', stacked=True)
plt.xlabel('Gender')
plt.ylabel('Count')
plt.title('Distribution of Gender by Gene Type')
plt.legend(title='Gene Type')
plt.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-45-c3edd67f7c54> in <cell line: 2>()
      1 # Group by 'Gender' and 'Gene Type' to see the distribution
----> 2 gender_distribution = df.groupby(['Gender', 'Gene Type']).size().unstack(fill_value=0)
      3 
      4 print(gender_distribution)
      5 

/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py in groupby(self, by, axis, level, as_index, sort, group_keys, observed, dropna)
   8867             raise TypeError("You have to supply one of 'by' and 'level'")
   8868 
-> 8869         return DataFrameGroupBy(
   8870             obj=self,
   8871             keys=by,

/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)
   1276 
   1277         if grouper is None:
-> 1278             grouper, exclusions, obj = get_grouper(
   1279                 obj,
   1280                 keys,

/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/grouper.py in get_grouper(obj, key, axis, level, sort, observed, validate, dropna)
   1002                     # non-unique columns; raise here to get the name in the
   1003                     # exception message
-> 1004                     raise ValueError(f"Grouper for '{name}' not 1-dimensional")
   1005                 exclusions.add(name)
   1006             elif obj._is_level_reference(gpr, axis=axis):

ValueError: Grouper for 'Gender' not 1-dimensional
In [ ]:
# Create a DataFrame with gender and ASD status
gender_asd_df = df[['Gender', 'Gene Type']].copy()
gender_asd_df['Gene Type'] = gender_asd_df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)

# Group by gender and calculate the mean ASD rate
gender_asd_summary = gender_asd_df.groupby('Gender').mean()
print(gender_asd_summary)

Empty DataFrame
Columns: [Gene Type]
Index: []
In [ ]:
from scipy.stats import chi2_contingency

# Create a contingency table
contingency_table = pd.crosstab(df['Gender'], df['Gene Type'])
print(contingency_table)

# Perform chi-square test
chi2_stat, p_val, _, _ = chi2_contingency(contingency_table)
print(f"Chi-Square Statistic: {chi2_stat}")
print(f"P-Value: {p_val}")

if p_val < 0.05:
    print("There is a significant association between gender and ASD.")
else:
    print("There is no significant association between gender and ASD.")

Empty DataFrame
Columns: []
Index: []

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-32-4efdde19d0c4> in <cell line: 8>()
      6 
      7 # Perform chi-square test
----> 8 chi2_stat, p_val, _, _ = chi2_contingency(contingency_table)
      9 print(f"Chi-Square Statistic: {chi2_stat}")
     10 print(f"P-Value: {p_val}")

/usr/local/lib/python3.10/dist-packages/scipy/stats/contingency.py in chi2_contingency(observed, correction, lambda_)
    331         raise ValueError("All values in `observed` must be nonnegative.")
    332     if observed.size == 0:
--> 333         raise ValueError("No data; `observed` has size 0.")
    334 
    335     expected = expected_freq(observed)

ValueError: No data; `observed` has size 0.
In [ ]:
import pandas as pd

# Load the dataset
file_path = '/content/X_with_sex.csv'
df = pd.read_csv(file_path)

# Assuming 'Gene Type' column exists in the dataset
# Display the first few rows to verify
print(df.head())

# Calculate the counts of males and females for ASD and Non-ASD
# First, make sure 'Sex' is properly mapped as 1 for male and 0 for female
df['Sex'] = df['Sex'].map({1: 'Male', 0: 'Female'})

# Group by 'Gene Type' and 'Sex' and count occurrences
gender_asd_counts = df[df['Gene Type'] == 'ASD'].groupby('Sex').size()
gender_non_asd_counts = df[df['Gene Type'] == 'Non-ASD'].groupby('Sex').size()

# Print out the counts
print("Counts of Males and Females associated with ASD:")
print(gender_asd_counts)
print("\nCounts of Males and Females associated with Non-ASD:")
print(gender_non_asd_counts)

# Calculate and display proportions
total_asd = gender_asd_counts.sum()
total_non_asd = gender_non_asd_counts.sum()

print("\nProportion of Males and Females associated with ASD:")
print(f"Male: {gender_asd_counts.get('Male', 0)} ({(gender_asd_counts.get('Male', 0) / total_asd) * 100:.2f}%)")
print(f"Female: {gender_asd_counts.get('Female', 0)} ({(gender_asd_counts.get('Female', 0) / total_asd) * 100:.2f}%)")

print("\nProportion of Males and Females associated with Non-ASD:")
print(f"Male: {gender_non_asd_counts.get('Male', 0)} ({(gender_non_asd_counts.get('Male', 0) / total_non_asd) * 100:.2f}%)")
print(f"Female: {gender_non_asd_counts.get('Female', 0)} ({(gender_non_asd_counts.get('Female', 0) / total_non_asd) * 100:.2f}%)")

   Ocx_8pcw_M_13058  M1C_S1C_8pcw_M_13058  AMY_8pcw_M_13058  MGE_8pcw_M_13058  \
0              4.93                  5.15              5.43              6.00   
1              2.86                  2.87              3.93              3.23   
2              1.46                  0.95              1.19              1.46   
3              3.81                  3.32              2.95              3.49   
4              5.04                  5.49              5.31              5.32   

   STC_8pcw_M_13058  URL_8pcw_M_13058  CGE_8pcw_M_13058  DTH_8pcw_M_13058  \
0              4.83              5.59              5.49              5.23   
1              3.54              3.84              2.84              4.15   
2              1.25              0.78              1.16              0.73   
3              3.12              2.56              3.44              2.87   
4              5.47              5.20              5.40              5.03   

   MFC_8pcw_M_13058  DFC_8pcw_M_13058  ...  CBC_40yrs_F_12304  \
0              4.65              4.59  ...               4.81   
1              2.91              3.11  ...               2.95   
2              1.38              1.57  ...               0.44   
3              3.71              3.92  ...               2.71   
4              4.85              4.49  ...               3.31   

   V1C_40yrs_F_12304  OFC_40yrs_F_12304  STC_40yrs_F_12304  IPC_40yrs_F_12304  \
0               6.02               6.16               6.15               6.18   
1               3.41               4.55               4.22               4.49   
2               0.88               0.41               0.44               0.52   
3               2.90               2.93               2.93               3.13   
4               3.56               3.65               3.59               3.73   

   M1C_40yrs_F_12304  HIP_40yrs_F_12304  STR_40yrs_F_12304  S1C_40yrs_F_12304  \
0               5.28               5.06               6.30               5.71   
1               2.90               4.17               3.30               3.56   
2               0.69               1.02               1.33               0.77   
3               3.34               3.41               3.30               3.38   
4               2.82               3.30               3.15               2.96   

   Sex  
0  1.0  
1  1.0  
2  1.0  
3  1.0  
4  1.0  

[5 rows x 525 columns]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py in get_loc(self, key)
   3790         try:
-> 3791             return self._engine.get_loc(casted_key)
   3792         except KeyError as err:

index.pyx in pandas._libs.index.IndexEngine.get_loc()

index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Gene Type'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-25-b2525e16cec5> in <cell line: 16>()
     14 
     15 # Group by 'Gene Type' and 'Sex' and count occurrences
---> 16 gender_asd_counts = df[df['Gene Type'] == 'ASD'].groupby('Sex').size()
     17 gender_non_asd_counts = df[df['Gene Type'] == 'Non-ASD'].groupby('Sex').size()
     18 

/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py in __getitem__(self, key)
   3891             if self.columns.nlevels > 1:
   3892                 return self._getitem_multilevel(key)
-> 3893             indexer = self.columns.get_loc(key)
   3894             if is_integer(indexer):
   3895                 indexer = [indexer]

/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py in get_loc(self, key)
   3796             ):
   3797                 raise InvalidIndexError(key)
-> 3798             raise KeyError(key) from err
   3799         except TypeError:
   3800             # If we have a listlike key, _check_indexing_error will raise

KeyError: 'Gene Type'
In [ ]:
# Assuming the columns are named 'Gene Type' and 'Sex'
# Ensure these columns exist
if 'Gene Type' in X_with_sex_loaded.columns and 'Sex' in X_with_sex_loaded.columns:
    # Filter the DataFrame for ASD cases (assuming 'Gene Type' = 1 for ASD)
    df_asd = X_with_sex_loaded[X_with_sex_loaded['Gene Type'] == 1]

    # Count the number of ASD cases by gender
    asd_counts_by_gender = df_asd['Sex'].value_counts()

    # Count the total number of cases by gender
    total_counts_by_gender = X_with_sex_loaded['Sex'].value_counts()

    # Compute the percentage of ASD cases by gender
    asd_percentage_by_gender = (asd_counts_by_gender / total_counts_by_gender) * 100

    # Print results
    print("Number of ASD cases by gender:")
    print(asd_counts_by_gender)

    print("\nTotal number of cases by gender:")
    print(total_counts_by_gender)

    print("\nPercentage of ASD cases by gender:")
    print(asd_percentage_by_gender)

    # Compare percentages
    male_asd_percentage = asd_percentage_by_gender.get(1, 0)
    female_asd_percentage = asd_percentage_by_gender.get(0, 0)

    print("\nComparison of ASD cases between males and females:")
    print(f"Percentage of ASD cases in males: {male_asd_percentage:.2f}%")
    print(f"Percentage of ASD cases in females: {female_asd_percentage:.2f}%")
else:
    print("The required columns are missing from the dataset.")

The required columns are missing from the dataset.
In [ ]:
# Save the updated dataset with the new 'Sex' feature
output_file_path = '/content/Updated_Supplementary_Tables.csv'
X_with_target = pd.concat([X, df[['Gene', 'Gene Type']]], axis=1)  # Combine features with target and gene columns
X_with_target.to_csv(output_file_path, index=False)

print(f"Updated dataset saved to {output_file_path}")
In [ ]:
# Define function to plot LIME explanations for both classes
def plot_lime_explanation(exp, class_name):
    exp.as_pyplot_figure(label=class_name)
    plt.title(f"Local explanation for class {class_name}")
    plt.show()

# Loop over the first few test instances
for i in range(3):  # You can adjust the range based on how many instances you want to explain
    exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)

    # Plot explanation for 'Non-ASD' class (class 0)
    plot_lime_explanation(exp, class_name=0)

    # Plot explanation for 'ASD' class (class 1)
    plot_lime_explanation(exp, class_name=1)

    print(f"Gene being explained: {genes_test.iloc[i]}")
    lime_prediction = model.predict([X_test_fs[i]])[0]
    actual_label = y_test.iloc[i]
    print(f"LIME Prediction: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual Label: {'ASD' if actual_label == 1 else 'Non-ASD'}")
    print(f"Prediction Match: {lime_prediction == actual_label}")
    print("-" * 40)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-3-5e31b2d4a8b3> in <cell line: 8>()
     10 
     11     # Plot explanation for 'Non-ASD' class (class 0)
---> 12     plot_lime_explanation(exp, class_name=0)
     13 
     14     # Plot explanation for 'ASD' class (class 1)

<ipython-input-3-5e31b2d4a8b3> in plot_lime_explanation(exp, class_name)
      1 # Define function to plot LIME explanations for both classes
      2 def plot_lime_explanation(exp, class_name):
----> 3     exp.as_pyplot_figure(label=class_name)
      4     plt.title(f"Local explanation for class {class_name}")
      5     plt.show()

/usr/local/lib/python3.10/dist-packages/lime/explanation.py in as_pyplot_figure(self, label, **kwargs)
    165         """
    166         import matplotlib.pyplot as plt
--> 167         exp = self.as_list(label=label, **kwargs)
    168         fig = plt.figure()
    169         vals = [x[1] for x in exp]

/usr/local/lib/python3.10/dist-packages/lime/explanation.py in as_list(self, label, **kwargs)
    139         """
    140         label_to_use = label if self.mode == "classification" else self.dummy_label
--> 141         ans = self.domain_mapper.map_exp_ids(self.local_exp[label_to_use], **kwargs)
    142         ans = [(x[0], float(x[1])) for x in ans]
    143         return ans

KeyError: 0
In [ ]:
# Dictionary to store cumulative feature contributions for the ASD class (class 1)
feature_contributions = {feature: 0 for feature in fs_feature_names}

# Loop over test instances
for i in range(len(X_test_fs)):  # Loop over the entire test set
    exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=len(fs_feature_names))

    # Check if the explanation for the ASD class (class index 1) exists
    if 1 in exp.local_exp:
        local_exp = exp.local_exp[1]  # Get the local explanation for the ASD class

        # Sum up the contributions of each feature
        for feature_idx, contribution in local_exp:
            feature_name = fs_feature_names[feature_idx]
            feature_contributions[feature_name] += contribution

# Sort features by their total contribution to the ASD class
sorted_contributions = sorted(feature_contributions.items(), key=lambda x: x[1], reverse=True)

# Plot the contributions
feature_names, contributions = zip(*sorted_contributions)
plt.figure(figsize=(10, 8))
plt.barh(feature_names, contributions, color='green')
plt.xlabel('Cumulative Contribution to ASD Class')
plt.title('Feature Contributions to ASD Class Across Test Set')
plt.gca().invert_yaxis()  # Reverse the order to have the largest at the top
plt.show()
In [ ]:
# Sort features by their total contribution to the ASD class
sorted_contributions = sorted(feature_contributions.items(), key=lambda x: x[1], reverse=True)

# Optionally, select top N features for display (e.g., top 20)
top_n = 20
sorted_contributions = sorted_contributions[:top_n]

# Plot the contributions
feature_names, contributions = zip(*sorted_contributions)
plt.figure(figsize=(8, 6))  # Increase the figure size
plt.barh(feature_names, contributions, color='green')
plt.xlabel('Cumulative Contribution to ASD Class')
plt.title(f'Top {top_n} Feature Contributions to ASD Class Across Test Set')
plt.gca().invert_yaxis()  # Reverse the order to have the largest at the top

# Rotate the labels to prevent overlap
plt.yticks(rotation=0, ha="right")

plt.show()
In [ ]:
# Assuming you have a dictionary feature_contributions where keys are feature names
# and values are contributions to ASD class

# Map feature names to genes
feature_gene_map = {feature: gene for feature, gene in zip(X.columns, genes_train)}

# Aggregate contributions by gene
gene_contributions = {}
for feature, contribution in feature_contributions.items():
    gene = feature_gene_map.get(feature, None)
    if gene:
        if gene not in gene_contributions:
            gene_contributions[gene] = 0
        gene_contributions[gene] += contribution

# Sort genes by their total contribution
sorted_gene_contributions = sorted(gene_contributions.items(), key=lambda x: x[1], reverse=True)

# Optionally, select top N genes for display (e.g., top 20)
top_n = 20
sorted_gene_contributions = sorted_gene_contributions[:top_n]
In [ ]:
import matplotlib.pyplot as plt

# Extract gene names and their contributions
gene_names, contributions = zip(*sorted_gene_contributions)

# Plot the contributions
plt.figure(figsize=(10, 8))  # Increase the figure size for better readability
plt.barh(gene_names, contributions, color='blue')
plt.xlabel('Cumulative Contribution to ASD Class')
plt.title(f'Top {top_n} Genes Contributing to ASD Class')
plt.gca().invert_yaxis()  # Reverse the order to have the largest at the top
plt.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-69-243e37614c57> in <cell line: 4>()
      2 
      3 # Extract gene names and their contributions
----> 4 gene_names, contributions = zip(*sorted_gene_contributions)
      5 
      6 # Plot the contributions

ValueError: not enough values to unpack (expected 2, got 0)
In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from lime import lime_tabular

# Load the dataset
file_path = '/content/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Save feature names for later
feature_names = X.columns

# Function to trim feature names
def trim_feature_name(name):
    parts = name.split('_')
    if len(parts) > 2:
        return f"{parts[0]}_{parts[1]}"
    else:
        return name  # Return as is if the format is unexpected

# Apply the function to all feature names
trimmed_feature_names = [trim_feature_name(name) for name in feature_names]

# Update the feature names in the dataset
X.columns = trimmed_feature_names

# Scale the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X_scaled, Y, genes, test_size=0.2, random_state=42)

# Define function to get feature importance
def get_feature_importance(X_train, y_train, model_name):
    if model_name == 'Lasso':
        model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    elif model_name == 'RandomForest':
        model = RandomForestClassifier(random_state=42)
    else:
        raise ValueError("Unknown model name")

    model.fit(X_train, y_train)
    if model_name == 'Lasso':
        importance = np.abs(model.coef_[0])
    else:
        importance = model.feature_importances_

    return importance

# Get feature importance from Lasso and Random Forest
lasso_importance = get_feature_importance(X_train, y_train, 'Lasso')
rf_importance = get_feature_importance(X_train, y_train, 'RandomForest')

# Combine the importance scores into a DataFrame
feature_importance_df = pd.DataFrame({
    'Feature': trimmed_feature_names,
    'Lasso Importance': lasso_importance,
    'Random Forest Importance': rf_importance
})

# Sort by Random Forest importance for display purposes
feature_importance_df.sort_values(by='Random Forest Importance', ascending=False, inplace=True)

# Display top 10 features
print(feature_importance_df.head(10))

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)

    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Evaluate the model
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = LogisticRegression(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Apply feature selection methods and model evaluation
X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train, y_train, X_test, trimmed_feature_names)
X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train, y_train, X_test, trimmed_feature_names)
X_train_pca, X_test_pca = fit_pca(X_train, X_test)
pca_feature_names = [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]

# Define models with trimmed feature names
models = {
    "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
    "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
    "PCA": (X_train_pca, X_test_pca, pca_feature_names),
    "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
}

for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
    model = evaluate_model(X_train_fs, X_test_fs, y_train, y_test, name, fs_feature_names)

    # Create a LIME explainer with trimmed feature names
    explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

    # Explain predictions for some examples in the test set
    for i in range(5):  # Explain the first 5 predictions
        lime_exp = explainer.explain_instance(X_test_fs[i], model.predict_proba)
        lime_exp.show_in_notebook(show_table=True)
        print(f"Sample {i+1}:")
        lime_exp.save_to_file(f'/content/lime_explanation_{name}_sample_{i+1}.html')

# Store the results in a CSV file
result_data = pd.DataFrame({
    'Gene': genes_test,
    'Actual': y_test,
    'Predicted': model.predict(X_test_fs)
})

# Save the results to a CSV file
result_data.to_csv('/content/gene_predictions.csv', index=False)
print(f"Results saved to /content/gene_predictions.csv")
In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from lime import lime_tabular

# Load the dataset
file_path = '/content/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Save feature names for later
feature_names = X.columns

# Function to trim feature names
def trim_feature_name(name):
    parts = name.split('_')
    if len(parts) > 2:
        return f"{parts[0]}_{parts[1]}"
    else:
        return name  # Return as is if the format is unexpected

# Apply the function to all feature names
trimmed_feature_names = [trim_feature_name(name) for name in feature_names]

# Update the feature names in the dataset
X.columns = trimmed_feature_names

# Scale the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X_scaled, Y, genes, test_size=0.2, random_state=42)

# Define function to get feature importance
def get_feature_importance(X_train, y_train, model_name):
    if model_name == 'Lasso':
        model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    elif model_name == 'RandomForest':
        model = RandomForestClassifier(random_state=42)
    else:
        raise ValueError("Unknown model name")

    model.fit(X_train, y_train)
    if model_name == 'Lasso':
        importance = np.abs(model.coef_[0])
    else:
        importance = model.feature_importances_

    return importance

# Get feature importance from Lasso and Random Forest
lasso_importance = get_feature_importance(X_train, y_train, 'Lasso')
rf_importance = get_feature_importance(X_train, y_train, 'RandomForest')

# Combine the importance scores into a DataFrame
feature_importance_df = pd.DataFrame({
    'Feature': trimmed_feature_names,
    'Lasso Importance': lasso_importance,
    'Random Forest Importance': rf_importance
})

# Sort by Random Forest importance for display purposes
feature_importance_df.sort_values(by='Random Forest Importance', ascending=False, inplace=True)

# Display top 10 features
print(feature_importance_df.head(10))

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)

    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Evaluate the model
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = LogisticRegression(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Apply feature selection methods and model evaluation
X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train, y_train, X_test, trimmed_feature_names)
X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train, y_train, X_test, trimmed_feature_names)
X_train_pca, X_test_pca = fit_pca(X_train, X_test)
pca_feature_names = [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]

# Define models with trimmed feature names
models = {
    "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
    "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
    "PCA": (X_train_pca, X_test_pca, pca_feature_names),
    "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
}

for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
    model = evaluate_model(X_train_fs, X_test_fs, y_train, y_test, name, fs_feature_names)

    # Create a LIME explainer with trimmed feature names
    explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

    # Explain a prediction
    for i in range(7):  # Explain the first 7 instances
        exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=10)
        exp.show_in_notebook(show_all=False)
        print(f"Gene being explained: {genes_test.iloc[i]}")

        # Show LIME prediction and compare with actual label
        lime_prediction = model.predict([X_test_fs[i]])[0]
        actual_label = y_test.iloc[i]
        print(f"LIME Predicted: {'ASD' if lime_prediction == 1 else 'Non-ASD'}, Actual: {'ASD' if actual_label == 1 else 'Non-ASD'}")

# Final results with full explanations and outputs are expected to be stored in a CSV file
result_data = pd.DataFrame({
    'Gene': genes_test,
    'Actual': y_test,
    'Predicted': model.predict(X_test)
})

# Save the results to a CSV file
result_data.to_csv('/content/gene_predictions.csv', index=False)
print(f"Results saved to /content/gene_predictions.csv")
In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from lime import lime_tabular

# Load the dataset
file_path = '/mnt/data/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Save feature names for later
feature_names = X.columns

# Function to trim feature names
def trim_feature_name(name):
    parts = name.split('_')
    if len(parts) > 2:
        return f"{parts[0]}_{parts[1]}"
    else:
        return name  # Return as is if the format is unexpected

# Apply the function to all feature names
trimmed_feature_names = [trim_feature_name(name) for name in feature_names]

# Update the feature names in the dataset
X.columns = trimmed_feature_names

# Scale the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X_scaled, Y, genes, test_size=0.2, random_state=42)

# Define function to get feature importance
def get_feature_importance(X_train, y_train, model_name):
    if model_name == 'Lasso':
        model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    elif model_name == 'RandomForest':
        model = RandomForestClassifier(random_state=42)
    else:
        raise ValueError("Unknown model name")

    model.fit(X_train, y_train)
    if model_name == 'Lasso':
        importance = np.abs(model.coef_[0])
    else:
        importance = model.feature_importances_

    return importance

# Get feature importance from Lasso and Random Forest
lasso_importance = get_feature_importance(X_train, y_train, 'Lasso')
rf_importance = get_feature_importance(X_train, y_train, 'RandomForest')

# Combine the importance scores into a DataFrame
feature_importance_df = pd.DataFrame({
    'Feature': trimmed_feature_names,
    'Lasso Importance': lasso_importance,
    'Random Forest Importance': rf_importance
})

# Sort by Random Forest importance for display purposes
feature_importance_df.sort_values(by='Random Forest Importance', ascending=False, inplace=True)

# Display top 10 features
print(feature_importance_df.head(10))

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)

    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Evaluate the model
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = LogisticRegression(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Apply feature selection methods and model evaluation
X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train, y_train, X_test, trimmed_feature_names)
X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train, y_train, X_test, trimmed_feature_names)
X_train_pca, X_test_pca = fit_pca(X_train, X_test)
pca_feature_names = [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]

# Define models with trimmed feature names
models = {
    "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
    "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
    "PCA": (X_train_pca, X_test_pca, pca_feature_names),
    "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
}

for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
    model = evaluate_model(X_train_fs, X_test_fs, y_train, y_test, name, fs_feature_names)

    # Create a LIME explainer with trimmed feature names
    explainer = lime_tabular.LimeTabularExplainer(X_train_fs, feature_names=fs_feature_names, class_names=['Non-ASD', 'ASD'], discretize_continuous=True)

    # Explain predictions for some examples in the test set
    for i in range(5):  # Explain the first 5 predictions
        lime_exp = explainer.explain_instance(X_test_fs[i], model.predict_proba)
        lime_exp.show_in_notebook(show_table=True)
        print(f"Sample {i+1}:")
        lime_exp.save_to_file(f'/mnt/data/lime_explanation_{name}_sample_{i+1}.html')

# Store the results in a CSV file
result_data = pd.DataFrame({
    'Gene': genes_test,
    'Actual': y_test,
    'Predicted': model.predict(X_test_fs)
})

# Save the results to a CSV file
result_data.to_csv('/mnt/data/gene_predictions.csv', index=False)
print(f"Results saved to /mnt/data/gene_predictions.csv")
In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN
from lime import lime_tabular
from lime.lime_tabular import LimeTabularExplainer


# Load the dataset
file_path = '/content/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Save feature names for later
feature_names = X.columns

# Function to trim feature names
def trim_feature_name(name):
    # Define the logic to trim the feature name
    # For example, keeping only the prefix up to the second underscore
    parts = name.split('_')
    if len(parts) > 2:
        return f"{parts[0]}_{parts[1]}"
    else:
        return name  # Return as is if the format is unexpected

# Apply the function to all feature names
trimmed_feature_names = [trim_feature_name(name) for name in feature_names]

# Update the feature names in the dataset
X.columns = trimmed_feature_names

# Scale the features
# scaler = StandardScaler()
# X_scaled = scaler.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X, Y, genes, test_size=0.2, random_state=42)

# Define sampling techniques
def apply_sampling(X_train, y_train, technique):
    if technique == 'RandomOverSampler':
        sampler = RandomOverSampler(random_state=42)
    elif technique == 'SMOTE':
        sampler = SMOTE(random_state=42)
    elif technique == 'ADASYN':
        sampler = ADASYN(random_state=42)
    else:
        raise ValueError("Unknown sampling technique")

    X_train_resampled, y_train_resampled = sampler.fit_resample(X_train, y_train)
    return X_train_resampled, y_train_resampled

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)
    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Evaluate the model
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = LogisticRegression(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Sampling techniques to apply
sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']

for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train, y_train, technique)

    # Apply feature selection methods
    X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)
    X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)

    # Apply PCA
    X_train_pca, X_test_pca = fit_pca(X_train_resampled, X_test)
    pca_feature_names = [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]

    # Define models with trimmed feature names
    models = {
        "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
        "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
        "PCA": (X_train_pca, X_test_pca, pca_feature_names),
        "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
    }

    for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
        model = evaluate_model(X_train_fs, X_test_fs, y_train_resampled, y_test, name, fs_feature_names)


# Set up the LIME explainer
explainer = LimeTabularExplainer(X_train.values,
                                 feature_names=trimmed_feature_names,
                                 class_names=['Non-ASD', 'ASD'],
                                 discretize_continuous=True)

# Function to get explanations from LIME
def get_lime_explanations(X_test, model, num_instances=100):
    lime_weights = pd.DataFrame(index=range(num_instances), columns=trimmed_feature_names)

    for i in range(num_instances):
        exp = explainer.explain_instance(X_test.iloc[i].values, model.predict_proba, num_features=len(trimmed_feature_names))
        exp_map = dict(exp.as_list())
        for feature in trimmed_feature_names:
            lime_weights.at[i, feature] = exp_map.get(feature, 0)

    return lime_weights

# Get explanations for a subset of the test data
num_instances_to_explain = 100  # You can adjust this number based on your test set size
lime_weights = get_lime_explanations(X_test, model, num_instances=num_instances_to_explain)

# Calculate the mean importance of each feature across all explained instances
mean_importances = lime_weights.abs().mean().sort_values(ascending=False)

# Plot the top 10 most important features
top_features = mean_importances.head(10)
plt.figure(figsize=(10, 6))
top_features.plot(kind='bar')
plt.title('Top 10 Most Influential Features According to LIME')
plt.ylabel('Average Absolute Contribution to Model Prediction')
plt.show()

# Optionally, print out the top features for inspection
print("Top 10 influential brain structures/features:")
print(top_features)

Applying RandomOverSampler

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Number of components retained: 7

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Lasso Feature Selection
Accuracy: 0.7559
MCC: 0.3405
AUC ROC: 0.7756
F1 Score: 0.4639
Sensitivity: 0.6338
Specificity: 0.6338
G Mean: 0.6338
----------------------------------------

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Random Forest Feature Selection
Accuracy: 0.7840
MCC: 0.4671
AUC ROC: 0.8583
F1 Score: 0.5534
Sensitivity: 0.8028
Specificity: 0.8028
G Mean: 0.8028
----------------------------------------

Model: PCA
Accuracy: 0.7418
MCC: 0.4438
AUC ROC: 0.8325
F1 Score: 0.5259
Sensitivity: 0.8592
Specificity: 0.8592
G Mean: 0.8592
----------------------------------------

Model: Random Forest with Grid Search
Accuracy: 0.8498
MCC: 0.4039
AUC ROC: 0.8493
F1 Score: 0.4839
Sensitivity: 0.4225
Specificity: 0.4225
G Mean: 0.4225
----------------------------------------

Applying SMOTE

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Number of components retained: 5

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Lasso Feature Selection
Accuracy: 0.7700
MCC: 0.4118
AUC ROC: 0.8069
F1 Score: 0.5149
Sensitivity: 0.7324
Specificity: 0.7324
G Mean: 0.7324
----------------------------------------

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Random Forest Feature Selection
Accuracy: 0.7559
MCC: 0.3933
AUC ROC: 0.8325
F1 Score: 0.5000
Sensitivity: 0.7324
Specificity: 0.7324
G Mean: 0.7324
----------------------------------------

Model: PCA
Accuracy: 0.7300
MCC: 0.3999
AUC ROC: 0.8079
F1 Score: 0.4978
Sensitivity: 0.8028
Specificity: 0.8028
G Mean: 0.8028
----------------------------------------

Model: Random Forest with Grid Search
Accuracy: 0.8169
MCC: 0.4124
AUC ROC: 0.8357
F1 Score: 0.5185
Sensitivity: 0.5915
Specificity: 0.5915
G Mean: 0.5915
----------------------------------------

Applying ADASYN

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Number of components retained: 6

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Lasso Feature Selection
Accuracy: 0.7488
MCC: 0.3617
AUC ROC: 0.7831
F1 Score: 0.4780
Sensitivity: 0.6901
Specificity: 0.6901
G Mean: 0.6901
----------------------------------------

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Random Forest Feature Selection
Accuracy: 0.7441
MCC: 0.3252
AUC ROC: 0.8085
F1 Score: 0.4523
Sensitivity: 0.6338
Specificity: 0.6338
G Mean: 0.6338
----------------------------------------

Model: PCA
Accuracy: 0.7394
MCC: 0.4108
AUC ROC: 0.8024
F1 Score: 0.5067
Sensitivity: 0.8028
Specificity: 0.8028
G Mean: 0.8028
----------------------------------------

Model: Random Forest with Grid Search
Accuracy: 0.8146
MCC: 0.4152
AUC ROC: 0.8323
F1 Score: 0.5212
Sensitivity: 0.6056
Specificity: 0.6056
G Mean: 0.6056
----------------------------------------

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-9b5e52d39633> in <cell line: 203>()
    201 # Get explanations for a subset of the test data
    202 num_instances_to_explain = 100  # You can adjust this number based on your test set size
--> 203 lime_weights = get_lime_explanations(X_test, model, num_instances=num_instances_to_explain)
    204 
    205 # Calculate the mean importance of each feature across all explained instances

<ipython-input-3-9b5e52d39633> in get_lime_explanations(X_test, model, num_instances)
    192 
    193     for i in range(num_instances):
--> 194         exp = explainer.explain_instance(X_test.iloc[i].values, model.predict_proba, num_features=len(trimmed_feature_names))
    195         exp_map = dict(exp.as_list())
    196         for feature in trimmed_feature_names:

/usr/local/lib/python3.10/dist-packages/lime/lime_tabular.py in explain_instance(self, data_row, predict_fn, labels, top_labels, num_features, num_samples, distance_metric, model_regressor)
    353         ).ravel()
    354 
--> 355         yss = predict_fn(inverse)
    356 
    357         # for classification, the model needs to provide a list of tuples - classes

/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py in predict_proba(self, X)
    863         check_is_fitted(self)
    864         # Check data
--> 865         X = self._validate_X_predict(X)
    866 
    867         # Assign chunk of trees to jobs

/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py in _validate_X_predict(self, X)
    597         Validate X whenever one tries to predict, apply, predict_proba."""
    598         check_is_fitted(self)
--> 599         X = self._validate_data(X, dtype=DTYPE, accept_sparse="csr", reset=False)
    600         if issparse(X) and (X.indices.dtype != np.intc or X.indptr.dtype != np.intc):
    601             raise ValueError("No support for np.int64 index based sparse matrices")

/usr/local/lib/python3.10/dist-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)
    624 
    625         if not no_val_X and check_params.get("ensure_2d", True):
--> 626             self._check_n_features(X, reset=reset)
    627 
    628         return out

/usr/local/lib/python3.10/dist-packages/sklearn/base.py in _check_n_features(self, X, reset)
    413 
    414         if n_features != self.n_features_in_:
--> 415             raise ValueError(
    416                 f"X has {n_features} features, but {self.__class__.__name__} "
    417                 f"is expecting {self.n_features_in_} features as input."

ValueError: X has 524 features, but RandomForestClassifier is expecting 157 features as input.
In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import (accuracy_score, matthews_corrcoef, roc_auc_score, f1_score,
                             confusion_matrix, recall_score)
from sklearn.feature_selection import SelectFromModel
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN
from lime.lime_tabular import LimeTabularExplainer

# Load the dataset
file_path = '/content/Supplementary_Tables.xlsx'
df = pd.read_excel(file_path, sheet_name='Supplementary Table S1')

# Separate the target variable and the feature set
genes = df['Gene']
X = df.drop(columns=['Gene', 'Ensembl ID', 'Gene Type'])  # Dropping only non-feature columns that aren't useful for prediction
Y = df['Gene Type'].apply(lambda x: 1 if x == 'ASD' else 0)  # Assuming 'Gene Type' is the target

# Save feature names for later
feature_names = X.columns

# Function to trim feature names
def trim_feature_name(name):
    parts = name.split('_')
    if len(parts) > 2:
        return f"{parts[0]}_{parts[1]}"
    else:
        return name  # Return as is if the format is unexpected

# Apply the function to all feature names
trimmed_feature_names = [trim_feature_name(name) for name in feature_names]

# Update the feature names in the dataset
X.columns = trimmed_feature_names

# Split the data into training and testing sets
X_train, X_test, y_train, y_test, genes_train, genes_test = train_test_split(X, Y, genes, test_size=0.2, random_state=42)

# Define sampling techniques
def apply_sampling(X_train, y_train, technique):
    if technique == 'RandomOverSampler':
        sampler = RandomOverSampler(random_state=42)
    elif technique == 'SMOTE':
        sampler = SMOTE(random_state=42)
    elif technique == 'ADASYN':
        sampler = ADASYN(random_state=42)
    else:
        raise ValueError("Unknown sampling technique")

    X_train_resampled, y_train_resampled = sampler.fit_resample(X_train, y_train)
    return X_train_resampled, y_train_resampled

# Define feature selection methods
def lasso_feature_selection(X_train, y_train, X_test, feature_names):
    lasso_model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
    lasso_model.fit(X_train, y_train)
    selector = SelectFromModel(lasso_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

def rf_feature_selection(X_train, y_train, X_test, feature_names):
    rf_model = RandomForestClassifier(random_state=42)
    rf_model.fit(X_train, y_train)
    selector = SelectFromModel(rf_model, threshold='mean')
    selected_indices = selector.get_support(indices=True)
    return selector.transform(X_train), selector.transform(X_test), [feature_names[i] for i in selected_indices]

# Define PCA transformation and fit on data
def fit_pca(X_train, X_test):
    pca = PCA(n_components=0.95)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)
    # Find out how many components were selected
    num_components = pca.n_components_
    print(f"Number of components retained: {num_components}")

    # Plot the cumulative explained variance
    plt.figure(figsize=(8, 6))
    plt.plot(np.cumsum(pca.explained_variance_ratio_))
    plt.xlabel('Number of Components')
    plt.ylabel('Cumulative Explained Variance')
    plt.title('Explained Variance by Number of Components')
    plt.grid(True)
    plt.show()

    return X_train_pca, X_test_pca

# Evaluate the model
def evaluate_model(X_train, X_test, y_train, y_test, model_name, feature_names):
    if model_name == "Random Forest with Grid Search":
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10]
        }
        grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                                   param_grid=param_grid,
                                   cv=5,
                                   scoring='accuracy')
        grid_search.fit(X_train, y_train)
        model = grid_search.best_estimator_
    else:
        model = LogisticRegression(random_state=42)
        model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    conf_matrix = confusion_matrix(y_test, y_pred)
    accuracy = accuracy_score(y_test, y_pred)
    mcc = matthews_corrcoef(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_prob)
    f1 = f1_score(y_test, y_pred)
    sensitivity = recall_score(y_test, y_pred)
    specificity = conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[1, 0]) if conf_matrix[1, 1] + conf_matrix[1, 0] != 0 else 0
    g_mean = (sensitivity * specificity) ** 0.5

    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"MCC: {mcc:.4f}")
    print(f"AUC ROC: {roc_auc:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print(f"Sensitivity: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"G Mean: {g_mean:.4f}")
    print("-" * 40)

    # Visualize the confusion matrix
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Non-ASD', 'ASD'], yticklabels=['Non-ASD', 'ASD'])
    plt.title(f'Confusion Matrix - {model_name}')
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.show()

    return model

# Sampling techniques to apply
sampling_techniques = ['RandomOverSampler', 'SMOTE', 'ADASYN']

for technique in sampling_techniques:
    print(f"Applying {technique}")

    # Apply sampling technique
    X_train_resampled, y_train_resampled = apply_sampling(X_train, y_train, technique)

    # Apply feature selection methods
    X_train_lasso, X_test_lasso, lasso_feature_names = lasso_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)
    X_train_rf, X_test_rf, rf_feature_names = rf_feature_selection(X_train_resampled, y_train_resampled, X_test, trimmed_feature_names)

    # Apply PCA
    X_train_pca, X_test_pca = fit_pca(X_train_resampled, X_test)
    pca_feature_names = [f"PCA Component {i+1}" for i in range(X_train_pca.shape[1])]

    # Define models with trimmed feature names
    models = {
        "Lasso Feature Selection": (X_train_lasso, X_test_lasso, lasso_feature_names),
        "Random Forest Feature Selection": (X_train_rf, X_test_rf, rf_feature_names),
        "PCA": (X_train_pca, X_test_pca, pca_feature_names),
        "Random Forest with Grid Search": (X_train_rf, X_test_rf, rf_feature_names)
    }

    # Function to get explanations from LIME
def get_lime_explanations(X_test_fs, model, feature_names, num_instances=100):
    # Create the LIME explainer with the appropriate feature set
    explainer = LimeTabularExplainer(X_test_fs,  # Passed as values for the LIME explainer
                                     feature_names=feature_names,
                                     class_names=['Non-ASD', 'ASD'],
                                     discretize_continuous=True)

    lime_weights = pd.DataFrame(index=range(num_instances), columns=feature_names)

    for i in range(num_instances):
        exp = explainer.explain_instance(X_test_fs[i], model.predict_proba, num_features=len(feature_names))
        exp_map = dict(exp.as_list())
        for feature in feature_names:
            lime_weights.at[i, feature] = exp_map.get(feature, 0)

    return lime_weights

# Update the loop to correctly pass the arguments:
for name, (X_train_fs, X_test_fs, fs_feature_names) in models.items():
    model = evaluate_model(X_train_fs, X_test_fs, y_train_resampled, y_test, name, fs_feature_names)

    # Corrected function call
    lime_weights = get_lime_explanations(X_test_fs, model, fs_feature_names, num_instances=100)

    # Calculate the mean importance of each feature across all explained instances
    mean_importances = lime_weights.abs().mean().sort_values(ascending=False)

    # Plot the top 10 most important features
    top_features = mean_importances.head(10)
    plt.figure(figsize=(10, 6))
    top_features.plot(kind='bar')
    plt.title(f'Top 10 Most Influential Features According to LIME - {name}')
    plt.ylabel('Average Absolute Contribution to Model Prediction')
    plt.show()

    # Optionally, print out the top features for inspection
    print(f"Top 10 influential brain structures/features according to LIME for {name}:")
    print(top_features)

Applying RandomOverSampler

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Number of components retained: 7

Applying SMOTE

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Number of components retained: 5

Applying ADASYN

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SelectFromModel was fitted without feature names
  warnings.warn(

Number of components retained: 6

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Lasso Feature Selection
Accuracy: 0.7488
MCC: 0.3617
AUC ROC: 0.7831
F1 Score: 0.4780
Sensitivity: 0.6901
Specificity: 0.6901
G Mean: 0.6901
----------------------------------------

Top 10 influential brain structures/features according to LIME for Lasso Feature Selection:
URL_8pcw    0.0
ITC_8yrs    0.0
DFC_8yrs    0.0
STR_8yrs    0.0
A1C_8yrs    0.0
MFC_8yrs    0.0
OFC_8yrs    0.0
MFC_8yrs    0.0
VFC_8yrs    0.0
AMY_8yrs    0.0
dtype: object

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Model: Random Forest Feature Selection
Accuracy: 0.7441
MCC: 0.3252
AUC ROC: 0.8085
F1 Score: 0.4523
Sensitivity: 0.6338
Specificity: 0.6338
G Mean: 0.6338
----------------------------------------

Top 10 influential brain structures/features according to LIME for Random Forest Feature Selection:
Ocx_8pcw    0.0
AMY_8yrs    0.0
CBC_8yrs    0.0
A1C_8yrs    0.0
MFC_8yrs    0.0
OFC_8yrs    0.0
MFC_8yrs    0.0
A1C_8yrs    0.0
VFC_8yrs    0.0
V1C_8yrs    0.0
dtype: object
Model: PCA
Accuracy: 0.7394
MCC: 0.4108
AUC ROC: 0.8024
F1 Score: 0.5067
Sensitivity: 0.8028
Specificity: 0.8028
G Mean: 0.8028
----------------------------------------

Top 10 influential brain structures/features according to LIME for PCA:
PCA Component 1    0.0
PCA Component 2    0.0
PCA Component 3    0.0
PCA Component 4    0.0
PCA Component 5    0.0
PCA Component 6    0.0
dtype: object
Model: Random Forest with Grid Search
Accuracy: 0.8146
MCC: 0.4152
AUC ROC: 0.8323
F1 Score: 0.5212
Sensitivity: 0.6056
Specificity: 0.6056
G Mean: 0.6056
----------------------------------------

Top 10 influential brain structures/features according to LIME for Random Forest with Grid Search:
Ocx_8pcw    0.0
AMY_8yrs    0.0
CBC_8yrs    0.0
A1C_8yrs    0.0
MFC_8yrs    0.0
OFC_8yrs    0.0
MFC_8yrs    0.0
A1C_8yrs    0.0
VFC_8yrs    0.0
V1C_8yrs    0.0
dtype: object